diff --git a/Data_classification/doc/Data_classification/Data_classification.txt b/Data_classification/doc/Data_classification/Data_classification.txt
new file mode 100644
index 00000000000..3b55c9143ff
--- /dev/null
+++ b/Data_classification/doc/Data_classification/Data_classification.txt
@@ -0,0 +1,26 @@
+namespace CGAL {
+/*!
+
+\mainpage User Manual
+\anchor Chapter_Data_Classification
+\cgalAutoToc
+\author Simon Giraudot
+
+This component implements an algorithm that classifies a point cloud into a user-defined set of classes (such as ground, vegetation, buildings, etc.). A flexible API is provided so that the user can compute its own local attributes on the point cloud and define its own classes based on these attributes.
+
+\section Data_classification_attributes Attributes
+
+\section Data_classification_types Classification types
+
+\section Data_classification_psc Point set classification
+
+\section Data_classification_custom Defining custom attributes and types
+
+\section Data_classification_example Example
+
+The following example reads an input file (LIDAR point set in PLY format); computes 4 attributes (scatter, elevation, horizontality and non-planarity) from this input; defines 3 classification types (vegetation, ground and roof) along with the effects of attributes on them; classifies the point set; recovers the segmented input in 3 separated arrays.
+
+\cgalExample{Data_classification/example_point_set_classification.cpp}
+
+*/
+} /* namespace CGAL */
diff --git a/Data_classification/doc/Data_classification/Doxyfile.in b/Data_classification/doc/Data_classification/Doxyfile.in
new file mode 100644
index 00000000000..dd26b7fbf64
--- /dev/null
+++ b/Data_classification/doc/Data_classification/Doxyfile.in
@@ -0,0 +1,5 @@
+@INCLUDE = ${CGAL_DOC_PACKAGE_DEFAULTS}
+PROJECT_NAME = "CGAL ${CGAL_DOC_VERSION} - Data Classification"
+INPUT        = ${CMAKE_SOURCE_DIR}/Data_classification/doc/Data_classification/ \
+               ${CMAKE_SOURCE_DIR}/Data_classification/include/CGAL/Data_classification/ \
+               ${CMAKE_SOURCE_DIR}/Data_classification/include/CGAL/Point_set_classification.h
\ No newline at end of file
diff --git a/Data_classification/doc/Data_classification/PackageDescription.txt b/Data_classification/doc/Data_classification/PackageDescription.txt
new file mode 100644
index 00000000000..463bdf86101
--- /dev/null
+++ b/Data_classification/doc/Data_classification/PackageDescription.txt
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+/*!
+\defgroup PkgDataClassification Point Set Shape Detection Reference
+
+\defgroup PkgDataClassificationConcepts Concepts
+\ingroup PkgDataClassification
+
+\defgroup PkgDataClassificationShapes Shapes
+\ingroup PkgDataClassification
+
+\addtogroup PkgDataClassification
+
+\cgalPkgDescriptionBegin{Data Classification, PkgDataClassificationSummary}
+\cgalPkgPicture{data_classif.png}
+
+\cgalPkgSummaryBegin
+\cgalPkgAuthors{Simon Giraudot}
+\cgalPkgDesc{This component implements an algorithm that classifies a point cloud into a user-defined set of classes (such as ground, vegetation, buildings, etc.). A flexible API is provided so that the user can compute its own local attributes on the point cloud and define its own classes based on these attributes.}
+\cgalPkgManuals{Chapter_Data_Classification, PkgDataClassification}
+\cgalPkgSummaryEnd
+
+\cgalPkgShortInfoBegin
+\cgalPkgSince{4.9}
+\cgalPkgBib{cgal:ovja-pssd}
+\cgalPkgDependsOn{\ref PkgDEPENDENCY}
+\cgalPkgLicense{\ref licensesGPL "GPL"}
+\cgalPkgDemo{Operations on Polyhedra,polyhedron_3.zip}
+\cgalPkgShortInfoEnd
+
+\cgalPkgDescriptionEnd
+
+\cgalClassifedRefPages
+
+## Main Classes ##
+- `CGAL::Point_set_classification<Kernel>`
+- `CGAL::Classification_type`
+
+## Segmentation attributes ##
+- `CGAL::Segmentation_attribute`
+- `CGAL::Segmentation_attribute_color<Kernel>`
+- `CGAL::Segmentation_attribute_elevation<Kernel>`
+- `CGAL::Segmentation_attribute_horizontality<Kernel>`
+- `CGAL::Segmentation_attribute_nonplanarity<Kernel>`
+- `CGAL::Segmentation_attribute_scatter<Kernel>`
+
+
+*/
diff --git a/Data_classification/doc/Data_classification/dependencies b/Data_classification/doc/Data_classification/dependencies
new file mode 100644
index 00000000000..a4d5f76715e
--- /dev/null
+++ b/Data_classification/doc/Data_classification/dependencies
@@ -0,0 +1,6 @@
+Manual
+Kernel_23
+STL_Extension
+Algebraic_foundations
+Circulator
+Stream_support
diff --git a/Data_classification/doc/Data_classification/examples.txt b/Data_classification/doc/Data_classification/examples.txt
new file mode 100644
index 00000000000..5434a310405
--- /dev/null
+++ b/Data_classification/doc/Data_classification/examples.txt
@@ -0,0 +1,3 @@
+/*!
+\example Data_classification/example_point_set_classification.cpp
+*/
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diff --git a/Data_classification/examples/Data_classification/example_point_set_classification.cpp b/Data_classification/examples/Data_classification/example_point_set_classification.cpp
new file mode 100644
index 00000000000..6844e46d26a
--- /dev/null
+++ b/Data_classification/examples/Data_classification/example_point_set_classification.cpp
@@ -0,0 +1,100 @@
+#include <cstdlib>
+#include <fstream>
+#include <iostream>
+#include <string>
+
+#include <CGAL/Simple_cartesian.h>
+#include <CGAL/Point_set_classification.h>
+#include <CGAL/IO/read_ply_points.h>
+
+typedef CGAL::Simple_cartesian<double> Kernel;
+typedef CGAL::Point_set_classification<Kernel> PSC;
+
+typedef CGAL::Segmentation_attribute_scatter<Kernel> Scatter;
+typedef CGAL::Segmentation_attribute_elevation<Kernel> Elevation;
+typedef CGAL::Segmentation_attribute_horizontality<Kernel> Horizontality;
+typedef CGAL::Segmentation_attribute_nonplanarity<Kernel> NonPlanarity;
+
+typedef Kernel::Point_3 Point;
+
+int main (int argc, char** argv)
+{
+  std::string filename (argc > 1 ? argv[1] : "data/b9.ply");
+  std::ifstream in (filename.c_str());
+  std::vector<Point> pts;
+
+  std::cerr << "Reading input" << std::endl;
+  if (!in
+      || !(CGAL::read_ply_points (in, std::back_inserter (pts))))
+    {
+      std::cerr << "Error: cannot read " << filename << std::endl;
+      return EXIT_FAILURE;
+    }
+
+  std::cerr << "Initializing" << std::endl;
+  PSC psc (pts.begin (), pts.end(), 0.8);
+  psc.initialization();
+
+  std::cerr << "Computing attributes" << std::endl;
+  // Attributes with user-defined weights
+  Scatter scat (psc, 0.26);
+  Elevation elev (psc, 0.08);
+  Horizontality horiz (psc, 0.13);
+  NonPlanarity plan (psc, 0.72);
+
+  // Add attributes to PSC
+  psc.add_segmentation_attribute (&scat);
+  psc.add_segmentation_attribute (&elev);
+  psc.add_segmentation_attribute (&horiz);
+  psc.add_segmentation_attribute (&plan);
+
+  // Create classification type and define how attributes affect them
+  CGAL::Classification_type ground ("ground");
+  ground.set_attribute_effect (&scat, CGAL::Classification_type::PENALIZED_ATT);
+  ground.set_attribute_effect (&elev, CGAL::Classification_type::PENALIZED_ATT);
+  ground.set_attribute_effect (&horiz, CGAL::Classification_type::PENALIZED_ATT);
+  ground.set_attribute_effect (&plan, CGAL::Classification_type::PENALIZED_ATT);
+
+  CGAL::Classification_type vege ("vegetation");
+  vege.set_attribute_effect (&scat, CGAL::Classification_type::FAVORED_ATT);
+  vege.set_attribute_effect (&elev, CGAL::Classification_type::NEUTRAL_ATT);
+  vege.set_attribute_effect (&horiz, CGAL::Classification_type::NEUTRAL_ATT);
+  vege.set_attribute_effect (&plan, CGAL::Classification_type::PENALIZED_ATT);
+  
+  CGAL::Classification_type roof ("roof");
+  roof.set_attribute_effect (&scat, CGAL::Classification_type::NEUTRAL_ATT);
+  roof.set_attribute_effect (&elev, CGAL::Classification_type::FAVORED_ATT);
+  roof.set_attribute_effect (&horiz, CGAL::Classification_type::NEUTRAL_ATT);
+  roof.set_attribute_effect (&plan, CGAL::Classification_type::NEUTRAL_ATT);
+
+  // Add types to PSC
+  psc.add_classification_type (&vege);
+  psc.add_classification_type (&ground);
+  psc.add_classification_type (&roof);
+
+  psc.classify (1); // Run with method=1 (global regularization with graphcut)
+
+  // Recover output
+  std::vector<Point> pts_ground, pts_vege, pts_roof;
+  for (std::size_t i = 0; i < pts.size(); ++ i)
+    {
+      Classification_type* type = psc.classification_type_of (i);
+      switch (type)
+        {
+        case &ground:
+          pts_ground.push_back (i);
+          break;
+        case &vege:
+          pts_vege.push_back (i);
+          break;
+        case &roof:
+          pts_roof.push_back (i);
+          break;
+        default:
+          std::cerr << "Error: unknown classification type" << std::endl;
+        }
+    }
+  
+  std::cerr << "All done" << std::endl;
+  return EXIT_SUCCESS;
+}
diff --git a/Data_classification/include/CGAL/Data_classification/Color.h b/Data_classification/include/CGAL/Data_classification/Color.h
new file mode 100644
index 00000000000..e35d48919a8
--- /dev/null
+++ b/Data_classification/include/CGAL/Data_classification/Color.h
@@ -0,0 +1,122 @@
+// Copyright (c) 2016  INRIA Sophia-Antipolis (France).
+// All rights reserved.
+//
+// This file is part of CGAL (www.cgal.org).
+// You can redistribute it and/or modify it under the terms of the GNU
+// General Public License as published by the Free Software Foundation,
+// either version 3 of the License, or (at your option) any later version.
+//
+// Licensees holding a valid commercial license may use this file in
+// accordance with the commercial license agreement provided with the software.
+//
+// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
+// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
+//
+// $URL$
+// $Id$
+//
+// Author(s)     : Simon Giraudot
+
+#ifndef CGAL_DATA_CLASSIFICATION_COLOR_H
+#define CGAL_DATA_CLASSIFICATION_COLOR_H
+
+#include <CGAL/array.h>
+
+namespace CGAL {
+namespace Data_classification {
+
+  /// \cond SKIP_IN_MANUAL
+  
+typedef CGAL::cpp11::array<unsigned char, 3> RGB_Color;
+typedef CGAL::cpp11::array<double, 3> HSV_Color;
+
+  
+inline HSV_Color rgb_to_hsv (const RGB_Color& c)
+{
+  double r = (double)(c[0]) / 255.;
+  double g = (double)(c[1]) / 255.;
+  double b = (double)(c[2]) / 255.;
+  double Cmax = (std::max) (r, (std::max) (g, b));
+  double Cmin = (std::min) (r, (std::min) (g, b));
+  double delta = Cmax - Cmin;
+  double H = 0.;
+  
+  if (delta != 0.)
+    {
+      if (Cmax == r)
+        H = 60. * (((int)((g - b) / delta)) % 6);
+      else if (Cmax == g)
+        H = 60. * (((b - r) / delta) + 2.);
+      else
+        H = 60. * (((r - g) / delta) + 4.);
+    }
+  if (H < 0.) H += 360.;
+  double S = (Cmax == 0. ? 0. : 100. * (delta / Cmax));
+  double V = 100. * Cmax;
+  HSV_Color out = {{ H, S, V }};
+  return out;
+}
+
+inline RGB_Color hsv_to_rgb (const HSV_Color& c)
+{
+  double h = c[0];
+  double s = c[1];
+  double v = c[2];
+  
+  s /= 100.;
+  v /= 100.;
+  double C = v*s;
+  int hh = (int)(h/60.);
+  double X = C * (1-CGAL::abs (hh % 2 - 1));
+  double r = 0, g = 0, b = 0;
+  
+  if( hh>=0 && hh<1 )
+    {
+      r = C;
+      g = X;
+    }
+  else if( hh>=1 && hh<2 )
+    {
+      r = X;
+      g = C;
+    }
+  else if( hh>=2 && hh<3 )
+    {
+      g = C;
+      b = X;
+    }
+  else if( hh>=3 && hh<4 )
+    {
+      g = X;
+      b = C;
+    }
+  else if( hh>=4 && hh<5 )
+    {
+      r = X;
+      b = C;
+    }
+  else
+    {
+      r = C;
+      b = X;
+    }
+  double m = v-C;
+  r += m;
+  g += m;
+  b += m;
+  r *= 255.0;
+  g *= 255.0;
+  b *= 255.0;
+
+  RGB_Color out = {{ (unsigned char)r, (unsigned char)g, (unsigned char)b }};
+  return out;
+}
+
+  /// \endcond
+
+} // namespace Data_classification
+} // namespace CGAL
+
+
+
+#endif // CGAL_DATA_CLASSIFICATION_COLOR_H
diff --git a/Data_classification/include/CGAL/Data_classification/Image.h b/Data_classification/include/CGAL/Data_classification/Image.h
new file mode 100644
index 00000000000..e5192ccda08
--- /dev/null
+++ b/Data_classification/include/CGAL/Data_classification/Image.h
@@ -0,0 +1,110 @@
+// Copyright (c) 2016  INRIA Sophia-Antipolis (France).
+// All rights reserved.
+//
+// This file is part of CGAL (www.cgal.org).
+// You can redistribute it and/or modify it under the terms of the GNU
+// General Public License as published by the Free Software Foundation,
+// either version 3 of the License, or (at your option) any later version.
+//
+// Licensees holding a valid commercial license may use this file in
+// accordance with the commercial license agreement provided with the software.
+//
+// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
+// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
+//
+// $URL$
+// $Id$
+//
+// Author(s)     : Simon Giraudot
+
+#ifndef CGAL_DATA_CLASSIFICATION_IMAGE_H
+#define CGAL_DATA_CLASSIFICATION_IMAGE_H
+
+namespace CGAL {
+namespace Data_classification {
+
+  /// \cond SKIP_IN_MANUAL
+  
+template <typename Type>
+class Image
+{
+  std::size_t m_width;
+  std::size_t m_height;
+  Type* m_raw;
+
+
+public:
+
+  Image () : m_width(0), m_height(0), m_raw (NULL)
+  {
+  }
+  
+  Image (std::size_t width, std::size_t height)
+    : m_width (width),
+      m_height (height)
+  {
+    if (m_width * m_height > 0)
+      m_raw = new Type[width * height];
+    else
+      m_raw = NULL;
+  }
+  
+  ~Image ()
+  {
+    if (m_raw != NULL)
+      delete[] m_raw;
+  }
+
+  Image (const Image& other)
+    : m_width (other.width()),
+      m_height (other.height())
+
+  {
+    if (m_width * m_height > 0)
+      {
+        m_raw = new Type[m_width * m_height];
+        std::copy (other.m_raw, other.m_raw + (m_width * m_height), this->m_raw);
+      }
+    else
+      m_raw = NULL;
+  }
+  Image& operator= (const Image& other)
+  {
+    if (m_raw != NULL)
+      delete[] m_raw;
+
+    m_raw = NULL;
+    m_width = other.width();
+    m_height = other.height();
+    if (m_width * m_height > 0)
+      {
+        m_raw = new Type[m_width * m_height];
+        std::copy (other.m_raw, other.m_raw + (m_width * m_height), this->m_raw);
+      }
+    
+    return *this;
+  }
+  
+  std::size_t width() const { return m_width; }
+  std::size_t height() const { return m_height; }
+
+  Type& operator() (const std::size_t& x, const std::size_t& y)
+  {
+    return m_raw[y * m_width + x];
+  }
+  const Type& operator() (const std::size_t& x, const std::size_t& y) const
+  {
+    return m_raw[y * m_width + x];
+  }
+  
+
+};
+
+  /// \endcond
+
+} // namespace Data_classification
+} // namespace CGAL
+
+
+
+#endif // CGAL_DATA_CLASSIFICATION_IMAGE_H
diff --git a/Data_classification/include/CGAL/Data_classification/Segmentation_attribute.h b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute.h
new file mode 100644
index 00000000000..9fe75a13f6a
--- /dev/null
+++ b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute.h
@@ -0,0 +1,68 @@
+#ifndef CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_H
+#define CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_H
+
+#include <vector>
+
+namespace CGAL {
+
+
+/*!
+  \ingroup PkgDataClassification
+
+  \brief Abstract class describing a segmentation attribute.
+
+  A segmentation attribute must have a unique ID and associate a
+  scalar value to each point index of the classification input.
+*/
+
+
+class Segmentation_attribute
+{
+public:
+  /// \cond SKIP_IN_MANUAL
+  virtual ~Segmentation_attribute() { }
+  /// \endcond
+
+  /*!
+    \brief Get the ID of the attribute.
+    \return the ID of the attribute
+  */
+  virtual std::string id() { return "abstract_attribute"; }
+
+  /*!
+    \brief Value taken by the attribute on the given point.
+
+    This method must be implemented by inherited classes.
+
+    \param pt_index Index of the point in the classification input
+
+    \return the value of the attribute on the point of index `pt_index`
+  */
+  virtual double value (std::size_t pt_index) = 0;
+
+  /// \cond SKIP_IN_MANUAL
+  virtual double favored (std::size_t pt_index) { return (1. - value (pt_index)); }
+  virtual double penalized (std::size_t pt_index) { return value (pt_index); }
+  //  virtual double ignored (std::size_t pt_index) { return std::min (favored(pt_index), penalized(pt_index)); }
+  virtual double ignored (std::size_t) { return 0.5; }
+
+  void compute_mean_max (std::vector<double>& vect, double& mean, double& max)
+  {
+    mean = 0.;
+    max = 0.;
+  
+    for (std::size_t i = 0; i < vect.size(); ++ i)
+      {
+        mean += vect[i];
+        if (vect[i] > max)
+          max = vect[i];
+      }
+    mean /= vect.size();
+
+  }
+  /// \endcond
+};
+
+}
+
+#endif // CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_H
diff --git a/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_color.h b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_color.h
new file mode 100644
index 00000000000..11a1794758a
--- /dev/null
+++ b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_color.h
@@ -0,0 +1,95 @@
+#ifndef CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_COLOR_H
+#define CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_COLOR_H
+
+#include <vector>
+
+#include <CGAL/Point_set_classification.h>
+
+namespace CGAL {
+
+  /*!
+    \ingroup PkgDataClassification
+
+    \brief Segmentation attribute based on colorimetric information.
+
+    If the input point cloud comes with colorimetric information, it
+    can be useful for classification purposes. This attribute computes
+    a distance between the color of a point and a user-defined color
+    region in the HSV color-space, defined by a Gaussian
+    distribution with user-defined mean and standard deviation values.
+
+    \tparam Kernel The geometric kernel used.
+
+  */
+template <typename Kernel>
+class Segmentation_attribute_color : public Segmentation_attribute
+{
+  
+  typedef Point_set_classification<Kernel> PSC;
+  typedef typename PSC::Image_float Image_float;
+  typedef typename Data_classification::RGB_Color RGB_Color;
+  typedef typename Data_classification::HSV_Color HSV_Color;
+  
+  std::vector<double> color_attribute;
+  double mean_h, mean_s, mean_v, sd_h, sd_s, sd_v;
+  
+public:
+  /// \cond SKIP_IN_MANUAL
+  double weight;
+  double mean;
+  double max;
+  /// \endcond
+
+  /*!
+    \brief Constructs an attribute based on the given color.
+
+    \param M The point set classification object
+    \param weight The relative weight of this attribute
+    \param mean_h Mean hue of the selected color
+    \param mean_s Mean saturation of the selected color
+    \param mean_v Mean value of the selected color
+    \param sd_h Standard deviation of the hue of the selected color
+    \param sd_s Standard deviation of the saturation of the selected color
+    \param sd_v Standard deviation of the value of the selected color
+
+    \note The default values describe a gray color region
+    corresponding to the color of a concrete road.
+  */
+  Segmentation_attribute_color (Point_set_classification<Kernel>& M,
+                                double weight,
+                                double mean_h = 156., double mean_s = 5., double mean_v = 76.,
+                                double sd_h = 70., double sd_s = 12., double sd_v = 8.4)
+    : mean_h (mean_h)
+    , mean_s (mean_s)
+    , mean_v (mean_v)
+    , sd_h (sd_h)
+    , sd_s (sd_s)
+    , sd_v (sd_v)
+    , weight (weight)
+  {
+
+    std::cerr << "Using colors " << mean_h << " " << mean_s << " " << mean_v << std::endl;
+    for(std::size_t i=0; i < M.HPS.size();i++)
+      {
+        HSV_Color c = Data_classification::rgb_to_hsv (M.HPS[i].color);
+        color_attribute.push_back (std::exp (-(c[0] - mean_h) * (c[0] - mean_h) / (2. * sd_h * sd_h))
+                                   * std::exp (-(c[1] - mean_s) * (c[1] - mean_s) / (2. * sd_s * sd_s))
+                                   * std::exp (-(c[2] - mean_v) * (c[2] - mean_v) / (2. * sd_v * sd_v)));
+      }
+    this->compute_mean_max (color_attribute, mean, max);
+  }
+
+
+  virtual double value (std::size_t pt_index)
+  {
+    return std::max (0., std::min (1., color_attribute[pt_index] / weight));
+  }
+
+  virtual std::string id() { return "color"; }
+
+};
+
+
+}
+
+#endif // CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_COLOR_H
diff --git a/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_elevation.h b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_elevation.h
new file mode 100644
index 00000000000..63c2ed73cd3
--- /dev/null
+++ b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_elevation.h
@@ -0,0 +1,411 @@
+#ifndef CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_ELEVATION_H
+#define CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_ELEVATION_H
+
+#include <vector>
+
+#include <CGAL/Point_set_classification.h>
+
+namespace CGAL {
+
+  /*!
+    \ingroup PkgDataClassification
+
+    \brief Segmentation attribute based on local elevation.
+
+    The local position of the ground can be computed for urban
+    scenes. This attribute computes the distance to the local
+    estimation of the ground.
+
+    \tparam Kernel The geometric kernel used.
+
+  */
+template <typename Kernel>
+class Segmentation_attribute_elevation : public Segmentation_attribute
+{
+  typedef Point_set_classification<Kernel> PSC;
+  typedef typename PSC::Image_float Image_float;
+  
+  std::vector<double> elevation_attribute;
+  
+public:
+  /// \cond SKIP_IN_MANUAL
+  double weight;
+  double mean;
+  double max;
+  /// \endcond
+
+  /*!
+    \brief Constructs the attribute.
+
+    \param M The point set classification object
+    \param weight The relative weight of this attribute
+    \param on_groups Select if the attribute is computed point-wise of group-wise
+  */
+  Segmentation_attribute_elevation (Point_set_classification<Kernel>& M,
+                                    double weight,
+                                    bool on_groups = false) : weight (weight)
+  {
+    if (on_groups)
+      {
+        std::vector<std::vector<typename Kernel::Point_3> > pts (M.groups.size());
+
+        for (std::size_t i = 0; i < M.HPS.size(); ++ i)
+          {
+            std::size_t g = M.HPS[i].group;
+            if (g == (std::size_t)(-1))
+              continue;
+            pts[g].push_back (M.HPS[i].position);
+          }
+        
+        std::vector<typename Kernel::Point_3> centroids;
+        for (std::size_t i = 0; i < pts.size(); ++ i)
+          centroids.push_back (CGAL::centroid (pts[i].begin(), pts[i].end()));
+
+        for (std::size_t i = 0; i < M.HPS.size(); ++ i)
+          {
+            std::size_t g = M.HPS[i].group;
+            if (g == (std::size_t)(-1))
+              elevation_attribute.push_back (0.);
+            else
+              elevation_attribute.push_back (centroids[g].z());
+          }
+        
+        this->compute_mean_max (elevation_attribute, mean, max);
+        return;
+      }
+    
+    //DEM
+    Image_float DEM(M.grid_HPS.width(),M.grid_HPS.height());
+    Image_float DEMtemp(M.grid_HPS.width(),M.grid_HPS.height());
+
+    for (int j=0;j<(int)DEM.height();j++){	
+      for (int i=0;i<(int)DEM.width();i++){
+		
+        double mean_height=0;
+        std::vector < double > list_Z;
+		
+        for(std::size_t k=0;k<M.grid_HPS(i,j).size();k++) list_Z.push_back(M.HPS[M.grid_HPS(i,j)[k]].position.z());
+			
+        if(list_Z.size()>0){
+			
+          std::sort(list_Z.begin(),list_Z.end());
+          int ind_k= (int)floor((double)(list_Z.size()*0.9)-0.5);
+          mean_height=list_Z[ind_k];
+        }
+
+        DEM(i,j)=(float)mean_height;
+        DEMtemp(i,j)=DEM(i,j);
+      }
+    }
+
+    std::size_t square = (std::size_t)(M.m_radius_neighbors / M.m_grid_resolution) + 1;
+
+    for (std::size_t j = 0; j < DEM.height(); j++){	
+      for (std::size_t i = 0; i < DEM.width(); i++){
+		
+        if((M.Mask(i,j))&&(DEM(i,j)==0)){
+				
+          double distance_tot=0;	
+          double val=0;
+          std::size_t squareXmin = (i < square ? 0 : i - square);
+          std::size_t squareXmax = std::min(DEM.width() - 1, i + square);
+          std::size_t squareYmin = (j < square ? 0 : j - square);
+          std::size_t squareYmax = std::min(DEM.height() - 1, j + square);
+			
+          for(std::size_t k = squareXmin; k <= squareXmax; k++){
+            for(std::size_t l = squareYmin; l <= squareYmax; l++){
+					
+              double distance=sqrt(pow((double)i-k,2)+pow((double)j-l,2))*M.m_grid_resolution;
+					
+              if((distance<=M.m_radius_neighbors)&&(DEM(k,l)>0)&&(distance!=0)){
+                double dista=distance*distance;
+                val=val+DEM(k,l)/dista;
+                distance_tot=distance_tot+1/dista;
+              }
+            }
+          }
+			
+          val=val/distance_tot;
+          DEMtemp(i,j)=(float)val;			
+        }
+      }
+    }
+
+    float scale_byte_min_dem = M.BBox_scan.zmax();
+    float scale_byte_max_dem = M.BBox_scan.zmin();
+
+    for (std::size_t j = 0; j < DEM.height(); j++){	
+      for (std::size_t i = 0; i < DEM.width(); i++){
+		
+        DEM(i,j)=DEMtemp(i,j);
+
+        if(M.Mask(i,j)){
+          if(DEM(i,j)>scale_byte_max_dem) scale_byte_max_dem=DEM(i,j);
+          if(DEM(i,j)<scale_byte_min_dem) scale_byte_min_dem=DEM(i,j);
+        }
+      }
+    }
+
+    //TODO: smooth the DEM
+    for (std::size_t j = 1; j < DEM.height()-1; j++){	
+      for (std::size_t i = 1; i < DEM.width()-1; i++){
+        if(M.Mask(i,j)){
+          //DEM(i,j)=
+        }
+      }
+    }
+
+    //DTM computation
+    std::size_t step=15;
+    square = (std::size_t)(M.m_radius_dtm/M.m_grid_resolution)+1;
+    Image_float toto(M.grid_HPS.width(),M.grid_HPS.height());
+    Image_float im_Zfront(M.grid_HPS.width(),M.grid_HPS.height());
+    M.DTM=toto;
+
+    //round 1
+    for (std::size_t j = 0; j < M.DTM.height(); j++){	
+      for (std::size_t i = 0; i < M.DTM.width(); i++){
+        M.DTM(i,j)=0;
+        im_Zfront(i,j)=0;
+      }
+    }
+
+
+    for (std::size_t j = step/2+1; j < M.DTM.height(); j = j+step){	
+      for (std::size_t i = step+1/2; i < M.DTM.width(); i = i+step){
+		
+        //storage of the points in the disk
+        std::vector<float> list_pointsZ;
+        std::size_t squareXmin = (i < square ? 0 : i - square);
+        std::size_t squareXmax = std::min(M.DTM.width() - 1, i + square);
+        std::size_t squareYmin = (j < square ? 0 : j - square);
+        std::size_t squareYmax = std::min(M.DTM.height() - 1, j + square);
+
+        for(std::size_t k = squareXmin; k <= squareXmax; k++){
+          for(std::size_t l = squareYmin; l <= squareYmax; l++){
+			
+            double distance=sqrt(pow((double)i-k,2)+pow((double)j-l,2))*M.m_grid_resolution;
+            if(distance<=M.m_radius_dtm){
+              for(int nb=0;nb<(int)M.grid_HPS(k,l).size();nb++) list_pointsZ.push_back(M.HPS[M.grid_HPS(k,l)[nb]].position.z());
+            }
+          }
+        }
+
+        //ordering of the points
+        float G1=0; 
+        float G2=0;
+
+        if(list_pointsZ.size()>0){
+          std::sort(list_pointsZ.begin(),list_pointsZ.end());
+          int ind_k2= (int)floor((double)(list_pointsZ.size()*0.6)-0.5);
+          int ind_k1= (int)floor((double)(list_pointsZ.size()*0.3)-0.5);
+          G1=list_pointsZ[ind_k1];
+          G2=list_pointsZ[ind_k2];
+        }
+		
+        float Gfront=(G1+G2)/2;	
+        for(int iter=0;iter<3;iter++){
+			
+          float G1_temp=0; 
+          float G2_temp=0;
+          int count1=0; 
+
+          for (std::size_t j=0;j<list_pointsZ.size();j++){
+            if(list_pointsZ[j]<=Gfront){
+              G1_temp+=list_pointsZ[j]; 
+              count1++;
+            }
+            else{G2_temp+=list_pointsZ[j];}
+          }
+
+          if(count1>0) G1=(float)G1_temp/count1;
+          if(count1<(int)list_pointsZ.size()) G2=(float)G2_temp/(list_pointsZ.size()-count1);
+          Gfront=(G1+G2)/2;
+
+        }
+
+        M.DTM(i,j)=G1;
+        im_Zfront(i,j)=Gfront;
+
+        //extension by duplication
+        std::size_t IsquareXmin = (i < step/2 ? 0 : i-step/2);
+        std::size_t IsquareXmax = std::min(M.DTM.width()-1,i+step/2);
+        std::size_t JsquareYmin = (j < step/2 ? 0 : j-step/2);
+        std::size_t JsquareYmax = std::min(M.DTM.height()-1,j+step/2);
+		
+        if(M.DTM.width()-1-IsquareXmax<step) IsquareXmax=M.DTM.width()-1;
+        if(M.DTM.height()-1-JsquareYmax<step) JsquareYmax=M.DTM.height()-1;
+        if(IsquareXmin<step) IsquareXmin=0;
+        if(JsquareYmin<step) JsquareYmin=0;
+
+        for(std::size_t k = IsquareXmin; k <= IsquareXmax; k++){
+          for(std::size_t l = JsquareYmin; l <= JsquareYmax; l++){
+            if(M.Mask(k,l)){
+              M.DTM(k,l)=G1;
+              im_Zfront(k,l)=Gfront;
+            }
+          }
+        }
+      }
+    }
+
+    //Gaussian smoothness
+    for(std::size_t ik = 0; ik < 10; ik++) {
+      for(std::size_t j = 0; j < M.DTM.height(); j++){ 
+        for (std::size_t i = 0; i < M.DTM.width(); i++) {
+		
+          std::size_t IsquareXmin = (i < 1 ? 0 : i-1);
+          std::size_t IsquareXmax = std::min(M.DTM.width()-1,i+1); 
+          std::size_t JsquareYmin = (j < 1 ? 0 : j-1);
+          std::size_t JsquareYmax = std::min(M.DTM.height()-1,j+1); 
+          int count=0; 
+
+          for(std::size_t k = IsquareXmin; k <= IsquareXmax; k++){ 
+            for(std::size_t l = JsquareYmin; l <= JsquareYmax; l++){ 
+              if(M.Mask(k,l)) count++;
+            }
+          }	
+			
+          if(count==9) im_Zfront(i,j)=(im_Zfront(i-1,j-1)+2*im_Zfront(i,j-1)+im_Zfront(i+1,j-1)+2*im_Zfront(i-1,j)+4*im_Zfront(i,j)+2*im_Zfront(i+1,j)+im_Zfront(i-1,j+1)+2*im_Zfront(i,j+1)+im_Zfront(i+1,j+1))/16;
+        }
+      }
+    }
+
+    //round 2 
+    std::vector < bool > test_ground;
+    for(int i=0;i<(int)M.HPS.size();i++){
+      int I=M.HPS[i].ind_x;
+      int J=M.HPS[i].ind_y;
+      bool test=false;
+      if(M.HPS[i].position.z()-im_Zfront(I,J)<=0) {test=true; test_ground.push_back(test);}
+      else{test_ground.push_back(test);}
+    }
+
+    for (int j=0;j<(int)M.DTM.height();j++){	
+      for (int i=0;i<(int)M.DTM.width();i++){
+        M.DTM(i,j)=0;
+        im_Zfront(i,j)=0;
+      }
+    }
+
+    for (std::size_t j = step/2+1; j < M.DTM.height(); j = j+step){	
+      for (std::size_t i = step/2+1; i < M.DTM.width(); i = i+step){
+		
+        //stockage des nouveaux points
+        std::vector < float > list_pointsZ;
+        std::size_t squareXmin = (i < square ? 0 : i-square);
+        std::size_t squareXmax = std::min(M.DTM.width()-1,i+square);
+        std::size_t squareYmin = (j < square ? 0 : j-square);
+        std::size_t squareYmax = std::min(M.DTM.height()-1,j+square);
+
+        for(std::size_t k = squareXmin; k <= squareXmax; k++){
+          for(std::size_t l = squareYmin; l <= squareYmax; l++){
+				
+            double distance=sqrt(pow((double)i-k,2)+pow((double)j-l,2))*M.m_grid_resolution;
+				
+            if(distance<=M.m_radius_dtm){
+					
+              for(int nb=0;nb<(int)M.grid_HPS(k,l).size();nb++){
+                if(test_ground[M.grid_HPS(k,l)[nb]]) list_pointsZ.push_back(M.HPS[M.grid_HPS(k,l)[nb]].position.z());
+              }
+            }
+          }
+        }
+
+        //ordering the new points
+        float G1=0; 
+        float G2=0;
+
+        if(list_pointsZ.size()>0){
+          std::sort(list_pointsZ.begin(),list_pointsZ.end());
+          int ind_k2= (int)floor((double)(list_pointsZ.size()*0.6)-0.5);
+          int ind_k1= (int)floor((double)(list_pointsZ.size()*0.3)-0.5);
+          G1=list_pointsZ[ind_k1];
+          G2=list_pointsZ[ind_k2];
+        }
+
+        float Gfront=(G1+G2)/2;	
+        for(int iter=0;iter<3;iter++){
+          float G1_temp=0; 
+          float G2_temp=0;
+          int count1=0; 
+
+          for (int j=0;j<(int)list_pointsZ.size();j++){
+            if(list_pointsZ[j]<=Gfront){G1_temp+=list_pointsZ[j]; count1++;}
+            else{G2_temp+=list_pointsZ[j];}
+          }
+          if(count1>0) G1=(float)G1_temp/count1;
+          if(count1<(int)list_pointsZ.size()) G2=(float)G2_temp/(list_pointsZ.size()-count1);
+          Gfront=(G1+G2)/2;
+        }
+        M.DTM(i,j)=G1;
+        im_Zfront(i,j)=Gfront;
+
+        //extension by duplication
+        std::size_t IsquareXmin = (i < step/2 ? 0 : i-step/2);
+        std::size_t IsquareXmax = std::min(M.DTM.width()-1,i+step/2);
+        std::size_t JsquareYmin = (j < step/2 ? 0 : j-step/2);
+        std::size_t JsquareYmax = std::min(M.DTM.height()-1,j+step/2);
+		
+        if(M.DTM.width()-1-IsquareXmax<step) IsquareXmax=M.DTM.width()-1;
+        if(M.DTM.height()-1-JsquareYmax<step) JsquareYmax=M.DTM.height()-1;
+        if(IsquareXmin<step) IsquareXmin=0;
+        if(JsquareYmin<step) JsquareYmin=0;
+
+        for(std::size_t k = IsquareXmin; k <= IsquareXmax; k++){
+          for(std::size_t l = JsquareYmin; l <= JsquareYmax; l++){
+            if(M.Mask(k,l)){
+              M.DTM(k,l)=G1; 
+              im_Zfront(k,l)=Gfront;
+            }
+          }
+        }
+      }
+    }
+
+    //Gaussian smoothness
+    for(std::size_t ik = 0; ik < 10; ik++) {
+      for(std::size_t j = 0; j < M.DTM.height(); j++){ 
+        for (std::size_t i = 0; i < M.DTM.width(); i++) {
+		
+          std::size_t IsquareXmin = (i < 1 ? 0 : i - 1);
+          std::size_t IsquareXmax = std::min(M.DTM.width()-1,i+1); 
+          std::size_t JsquareYmin = (j < 1 ? 0 : j - 1);
+          std::size_t JsquareYmax = std::min(M.DTM.height()-1,j+1); 
+          int count=0; 
+
+          for(std::size_t k=IsquareXmin; k<=IsquareXmax; k++){ 
+            for(std::size_t l=JsquareYmin; l<=JsquareYmax; l++){ 
+              if(M.Mask(k,l)) count++;
+            }
+          }	
+          if(count==9) M.DTM(i,j)=(M.DTM(i-1,j-1)+2*M.DTM(i,j-1)+M.DTM(i+1,j-1)+2*M.DTM(i-1,j)+4*M.DTM(i,j)+2*M.DTM(i+1,j)+M.DTM(i-1,j+1)+2*M.DTM(i,j+1)+M.DTM(i+1,j+1))/16;
+        }
+      }
+    }
+
+    //ranger les valeurs dans scans lidars
+    for(int i=0;i<(int)M.HPS.size();i++){
+      int I=M.HPS[i].ind_x;
+      int J=M.HPS[i].ind_y;
+      elevation_attribute.push_back ((double)(M.HPS[i].position.z()-M.DTM(I,J)));
+    }
+
+    this->compute_mean_max (elevation_attribute, mean, max);
+    //    max *= 5;
+  }
+
+  virtual double value (std::size_t pt_index)
+  {
+    return std::max (0., std::min (1., elevation_attribute[pt_index] / weight));
+  }
+  
+  virtual std::string id() { return "elevation"; }
+  
+};
+  
+
+
+}
+
+#endif // CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_ELEVATION_H
diff --git a/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_horizontality.h b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_horizontality.h
new file mode 100644
index 00000000000..8c05b740688
--- /dev/null
+++ b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_horizontality.h
@@ -0,0 +1,89 @@
+#ifndef CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_HORIZONTALITY_H
+#define CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_HORIZONTALITY_H
+
+#include <vector>
+
+#include <CGAL/Point_set_classification.h>
+
+namespace CGAL {
+
+  /*!
+    \ingroup PkgDataClassification
+
+    \brief Segmentation attribute based on local horizontality.
+
+    The orientation of the best fitting plane of a local neighborhood
+    of the considered point can be useful to disciminate facades from
+    the ground.
+
+    \tparam Kernel The geometric kernel used.
+
+  */
+template <typename Kernel>
+class Segmentation_attribute_horizontality : public Segmentation_attribute
+{
+  typedef Point_set_classification<Kernel> PSC;
+
+  std::vector<double> horizontality_attribute;
+  
+public:
+  /// \cond SKIP_IN_MANUAL
+  double weight;
+  double mean;
+  double max;
+  /// \endcond
+  
+  /*!
+    \brief Constructs the attribute.
+
+    \param M The point set classification object
+    \param weight The relative weight of this attribute
+    \param on_groups Select if the attribute is computed point-wise of group-wise
+  */
+  Segmentation_attribute_horizontality (Point_set_classification<Kernel>& M, double weight,
+                                        bool on_groups = false) : weight (weight)
+  {
+    typename Kernel::Vector_3 vertical (0., 0., 1.);
+
+    if (on_groups)
+      {
+        std::vector<double> values;
+        
+        values.reserve (M.groups.size());
+        for (std::size_t i = 0; i < M.groups.size(); ++ i)
+          {
+            typename Kernel::Vector_3 normal = M.groups[i].orthogonal_vector();
+            normal = normal / std::sqrt (normal * normal);
+            values.push_back (1. - std::fabs(normal * vertical));
+          }
+        for (std::size_t i = 0; i < M.HPS.size(); ++ i)
+          if (M.HPS[i].group == (std::size_t)(-1))
+            horizontality_attribute.push_back (0.5);
+          else
+            horizontality_attribute.push_back (values[M.HPS[i].group]);
+      }
+    else
+      {
+        for(int i=0; i<(int)M.HPS.size(); i++)
+          {
+            typename Kernel::Vector_3 normal = M.planes[i].orthogonal_vector();
+            normal = normal / std::sqrt (normal * normal);
+            horizontality_attribute.push_back (1. - std::fabs(normal * vertical));
+          }
+      }
+    
+    this->compute_mean_max (horizontality_attribute, mean, max);
+    //    max *= 2;
+  }
+  virtual double value (std::size_t pt_index)
+  {
+    return std::max (0., std::min (1., horizontality_attribute[pt_index] / weight));
+  }
+
+  virtual std::string id() { return "horizontality"; }
+};
+
+
+}
+
+#endif // CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_HORIZONTALITY_H
diff --git a/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_nonplanarity.h b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_nonplanarity.h
new file mode 100644
index 00000000000..12fbc35a6c9
--- /dev/null
+++ b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_nonplanarity.h
@@ -0,0 +1,92 @@
+#ifndef CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_NONPLANARITY_H
+#define CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_NONPLANARITY_H
+
+#include <vector>
+
+#include <CGAL/Point_set_classification.h>
+
+namespace CGAL {
+
+  /*!
+    \ingroup PkgDataClassification
+
+    \brief Segmentation attribute based on local non-planarity.
+
+    Characterizing a level of non-planarity can help identify noisy
+    parts of the input such as vegetation. This attribute computes the
+    distance of a point to a locally fitted plane.
+    
+    \tparam Kernel The geometric kernel used.
+
+  */
+template <typename Kernel>
+class Segmentation_attribute_nonplanarity : public Segmentation_attribute
+{
+  typedef Point_set_classification<Kernel> PSC;
+
+  std::vector<double> distance_to_plane_attribute;
+  
+public:
+  /// \cond SKIP_IN_MANUAL
+  double weight;
+  double mean;
+  double max;
+  /// \endcond
+  
+  
+  /*!
+    \brief Constructs the attribute.
+
+    \param M The point set classification object
+    \param weight The relative weight of this attribute
+    \param on_groups Select if the attribute is computed point-wise of group-wise
+  */
+  Segmentation_attribute_nonplanarity (Point_set_classification<Kernel>& M, double weight, bool on_groups = false) : weight (weight)
+  {
+    if (on_groups)
+      {
+        std::vector<double> sq_dist (M.groups.size(), 0.);
+        std::vector<std::size_t> nb (M.groups.size(), 0);
+        for (std::size_t i = 0; i < M.HPS.size(); ++ i)
+          {
+            std::size_t g = M.HPS[i].group;
+            if (g == (std::size_t)(-1))
+              continue;
+            sq_dist[g] += CGAL::squared_distance (M.groups[g], M.HPS[i].position);
+            nb[g] ++;
+          }
+        for (std::size_t i = 0; i < sq_dist.size(); ++ i)
+          if (nb[i] > 0)
+            //            sq_dist[i] = std::sqrt (sq_dist[i] / nb[i]);
+            sq_dist[i] = 1. / nb[i];
+        for (std::size_t i = 0; i < M.HPS.size(); ++ i)
+          {
+            std::size_t g = M.HPS[i].group;
+            if (g == (std::size_t)(-1))
+              distance_to_plane_attribute.push_back (0.);
+            else
+              distance_to_plane_attribute.push_back (sq_dist[g]);
+          }
+      }
+    else
+      {
+        for(int i=0; i<(int)M.HPS.size(); i++)
+          distance_to_plane_attribute.push_back (std::sqrt (CGAL::squared_distance (M.HPS[i].position, M.planes[i])));
+      }
+    
+    this->compute_mean_max (distance_to_plane_attribute, mean, max);
+    //    max *= 2;
+  }
+
+  virtual double value (std::size_t pt_index)
+  {
+    return std::max (0., std::min (1., distance_to_plane_attribute[pt_index] / weight));
+  }
+
+  virtual std::string id() { return "nonplanarity"; }
+};
+
+
+}
+
+#endif // CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_NONPLANARITY_H
diff --git a/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_scatter.h b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_scatter.h
new file mode 100644
index 00000000000..a407ec4b4da
--- /dev/null
+++ b/Data_classification/include/CGAL/Data_classification/Segmentation_attribute_scatter.h
@@ -0,0 +1,268 @@
+#ifndef CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_SCATTER_H
+#define CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_SCATTER_H
+
+#include <vector>
+
+#include <CGAL/Point_set_classification.h>
+#include <CGAL/Delaunay_triangulation_2.h>
+#include <CGAL/Alpha_shape_2.h>
+
+namespace CGAL {
+
+  /*!
+    \ingroup PkgDataClassification
+
+    \brief Segmentation attribute based on local vertical dispersion of points.
+
+    Urban scenes can usually be described as a set of 2D regions with
+    different heights. While these heights are usually piecewise
+    constant or piecewise linear, on some specific parts of the scene
+    such as vegetation, they can become extremely unstable. This
+    attribute quantifies the vertical dispersion of the points on a local
+    Z-cylinder around the points.
+
+    \tparam Kernel The geometric kernel used.
+
+  */
+template <typename Kernel>
+class Segmentation_attribute_scatter : public Segmentation_attribute
+{
+  typedef Point_set_classification<Kernel> PSC;
+  typedef typename PSC::Image_float Image_float;
+  
+  std::vector<double> vegetation_attribute;
+  
+public:
+  /// \cond SKIP_IN_MANUAL
+  double weight;
+  double mean;
+  double max;
+  /// \endcond
+
+  /*!
+    \brief Constructs the attribute.
+
+    \param M The point set classification object
+    \param weight The relative weight of this attribute
+    \param on_groups Select if the attribute is computed point-wise of group-wise
+  */
+  Segmentation_attribute_scatter (Point_set_classification<Kernel>& M, double weight, bool on_groups = false) : weight (weight)
+  {
+    if (on_groups)
+      scatter_on_groups(M);
+    else if(M.is_echo_given)
+      scatter_with_echo(M);
+    else
+      scatter_with_vertical_dispersion(M);
+
+
+    this->compute_mean_max (vegetation_attribute, mean, max);
+    //    max *= 2;
+  }
+
+  // \cond SKIP_IN_MANUAL
+  typename Kernel::Point_2 to_2d (typename Kernel::Point_3 point,
+                                  typename Kernel::Plane_3 plane) const
+  {
+    typename Kernel::Vector_3 base1 = plane.base1() / std::sqrt (plane.base1() * plane.base1 ());
+    typename Kernel::Vector_3 base2 = plane.base2() / std::sqrt (plane.base2() * plane.base2 ());
+    typename Kernel::Point_3 origin = plane.projection (CGAL::ORIGIN);
+    typename Kernel::Vector_3 v (origin, point);
+    return typename Kernel::Point_2 (v * base1, v * base2);
+  }
+
+  
+  void scatter_on_groups (PSC& M)
+  {
+    std::vector<std::vector<typename Kernel::Point_2> > pts (M.groups.size());
+    
+    for (std::size_t i = 0; i < M.HPS.size(); ++i)
+      {
+        if (M.HPS[i].group == (std::size_t)(-1))
+          continue;
+        std::size_t g = M.HPS[i].group;
+        const typename Kernel::Plane_3& plane = M.groups[g];
+
+        pts[g].push_back (to_2d (M.HPS[i].position, plane));
+      }
+
+    std::vector<double> values;
+    // Alpha shapes
+    typedef CGAL::Alpha_shape_vertex_base_2<Kernel> Vb;
+    typedef CGAL::Alpha_shape_face_base_2<Kernel>  Fb;
+    typedef CGAL::Triangulation_data_structure_2<Vb,Fb> Tds;
+    typedef CGAL::Delaunay_triangulation_2<Kernel,Tds> Triangulation_2;
+    typedef CGAL::Alpha_shape_2<Triangulation_2>  Alpha_shape_2;
+
+    for (std::size_t i = 0; i < M.groups.size(); ++ i)
+      {
+        Alpha_shape_2 ashape (pts[i].begin (), pts[i].end (), M.m_grid_resolution);
+
+        double perimeter = 0.;
+        for (typename Alpha_shape_2::Finite_edges_iterator it = ashape.finite_edges_begin ();
+             it != ashape.finite_edges_end (); ++ it)
+          if (ashape.classify(*it) == Alpha_shape_2::REGULAR)
+            perimeter +=
+              (std::sqrt (CGAL::squared_distance (it->first->vertex ((it->second + 1) % 3)->point(),
+                                                  it->first->vertex ((it->second + 2) % 3)->point())));
+
+        double area = 0.;
+        for (typename Alpha_shape_2::Finite_faces_iterator it = ashape.finite_faces_begin ();
+             it != ashape.finite_faces_end (); ++ it)
+          if (ashape.classify(it) == Alpha_shape_2::INTERIOR)
+            area +=
+              (std::fabs (CGAL::area (it->vertex(0)->point(), it->vertex(1)->point(), it->vertex(2)->point())));
+        
+        //        values.push_back (ashape.number_of_solid_components() / (double)(pts[i].size()));
+        if (area == 0)
+          values.push_back (0.);
+        else
+          values.push_back (perimeter / pts[i].size());
+      }
+
+    for (std::size_t i = 0; i < M.HPS.size(); ++i)
+      if (M.HPS[i].group == (std::size_t)(-1))
+        vegetation_attribute.push_back (0.);
+      else
+        vegetation_attribute.push_back (values[M.HPS[i].group]);
+  }
+
+  void scatter_with_echo(PSC& M)
+  {
+    Image_float Vegetation(M.grid_HPS.width(), M.grid_HPS.height());
+    for (int j=0;j<(int)M.DTM.height();j++)	
+      for (int i=0;i<(int)M.DTM.width();i++)
+        Vegetation(i,j)=0;
+
+    std::size_t square = (std::size_t)(0.5 * M.m_radius_neighbors / M.m_grid_resolution) + 1;
+        
+    std::cerr << "Echo given" << std::endl;				
+    for (std::size_t j = 0; j < M.grid_HPS.height(); j++){	
+      for (std::size_t i = 0; i < M.grid_HPS.width(); i++){
+						
+        if(M.Mask(i,j)){
+
+          std::size_t squareXmin = (i < square ? 0 : i - square);
+          std::size_t squareXmax = std::min (M.grid_HPS.width()-1, i + square);
+          std::size_t squareYmin = (j < square ? 0 : j - square);
+          std::size_t squareYmax = std::min (M.grid_HPS.height()-1, j + square);
+			
+          int NB_echo_sup=0;
+          int NB_echo_total=0;
+
+          for(std::size_t k = squareXmin; k <= squareXmax; k++){
+            for(std::size_t l = squareYmin; l <= squareYmax; l++){
+									
+              if(sqrt(pow((double)k-i,2)+pow((double)l-j,2))<=(double)0.5*M.m_radius_neighbors/M.m_grid_resolution){
+										
+                if(M.grid_HPS(k,l).size()>0){
+									
+                  for(int t=0; t<(int)M.grid_HPS(k,l).size();t++){
+												
+                    int ip = M.grid_HPS(k,l)[t]; 
+                    if(M.HPS[ip].echo>1) NB_echo_sup++;
+                  }
+									
+                  NB_echo_total=NB_echo_total+M.grid_HPS(k,l).size();
+									
+                }
+							
+              }
+						
+            }
+					
+          }
+					
+          Vegetation(i,j)=(float)NB_echo_sup/NB_echo_total;
+				
+        }
+			
+      }
+		
+    }
+    for(int i=0;i<(int)M.HPS.size();i++){
+      int I= M.HPS[i].ind_x;
+      int J= M.HPS[i].ind_y;
+      vegetation_attribute.push_back((double)Vegetation(I,J));
+    }
+  }
+
+  void scatter_with_vertical_dispersion(PSC& M)
+  {
+    Image_float Vegetation(M.grid_HPS.width(), M.grid_HPS.height());
+    for (int j=0;j<(int)M.DTM.height();j++)	
+      for (int i=0;i<(int)M.DTM.width();i++)
+        Vegetation(i,j)=0;
+    std::size_t square = (std::size_t)(0.5 * M.m_radius_neighbors / M.m_grid_resolution) + 1;
+    typename Kernel::Vector_3 verti (0., 0., 1.);
+        
+    for (std::size_t j = 0; j < M.grid_HPS.height(); j++){	
+      for (std::size_t i = 0; i < M.grid_HPS.width(); i++){
+						
+        if(!(M.Mask(i,j)))
+          continue;
+        std::vector<double> hori;
+            
+        std::size_t squareXmin = (i < square ? 0 : i - square);
+        std::size_t squareXmax = std::min (M.grid_HPS.width()-1, i + square);
+        std::size_t squareYmin = (j < square ? 0 : j - square);
+        std::size_t squareYmax = std::min (M.grid_HPS.height()-1, j + square);
+
+        for(std::size_t k = squareXmin; k <= squareXmax; k++)
+          for(std::size_t l = squareYmin; l <= squareYmax; l++)
+            if(sqrt(pow((double)k-i,2)+pow((double)l-j,2))
+               <=(double)0.5*M.m_radius_neighbors/M.m_grid_resolution
+               && (M.grid_HPS(k,l).size()>0))
+              for(int t=0; t<(int)M.grid_HPS(k,l).size();t++)
+                {
+                  int ip = M.grid_HPS(k,l)[t];
+                  hori.push_back (M.HPS[ip].position.z());
+                }
+        if (hori.empty())
+          continue;
+              
+        std::sort (hori.begin(), hori.end());
+
+        std::size_t nb_layers = 1;
+
+        std::vector<bool> occupy (1 + (std::size_t)((hori.back() - hori.front()) / M.m_grid_resolution), false);
+              
+        std::size_t last_index = 0;
+        for (std::size_t k = 0; k < hori.size(); ++ k)
+          {
+            std::size_t index = (std::size_t)((hori[k] - hori.front()) / M.m_grid_resolution);
+            occupy[index] = true;
+            if (index > last_index + 1)
+              ++ nb_layers;
+            last_index = index;
+          }
+
+        std::size_t nb_occ = 0;
+        for (std::size_t k = 0; k < occupy.size(); ++ k)
+          if (occupy[k])
+            ++ nb_occ;
+					
+        Vegetation(i,j)= 1.f - (nb_occ / (float)(occupy.size()));
+			
+      }
+		
+    }
+    for(int i=0;i<(int)M.HPS.size();i++){
+      int I= M.HPS[i].ind_x;
+      int J= M.HPS[i].ind_y;
+      vegetation_attribute.push_back((double)Vegetation(I,J));
+    }
+  }
+  /// \endcond
+  
+  virtual double value (std::size_t pt_index)
+  {
+    return std::max (0., std::min (1., vegetation_attribute[pt_index] / weight));
+  }
+
+  virtual std::string id() { return "scatter"; }
+};
+
+}
+
+#endif // CGAL_DATA_CLASSIFICATION_SEGMENTATION_ATTRIBUTE_SCATTER_H
diff --git a/Data_classification/include/CGAL/Point_set_classification.h b/Data_classification/include/CGAL/Point_set_classification.h
new file mode 100644
index 00000000000..5c6c39f9ea5
--- /dev/null
+++ b/Data_classification/include/CGAL/Point_set_classification.h
@@ -0,0 +1,1179 @@
+// Copyright (c) 2016  INRIA Sophia-Antipolis (France).
+// All rights reserved.
+//
+// This file is part of CGAL (www.cgal.org).
+// You can redistribute it and/or modify it under the terms of the GNU
+// General Public License as published by the Free Software Foundation,
+// either version 3 of the License, or (at your option) any later version.
+//
+// Licensees holding a valid commercial license may use this file in
+// accordance with the commercial license agreement provided with the software.
+//
+// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
+// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
+//
+// $URL$
+// $Id$
+//
+// Author(s)     : Florent Lafarge, Simon Giraudot
+
+#ifndef CGAL_POINT_SET_CLASSIFICATION_H
+#define CGAL_POINT_SET_CLASSIFICATION_H
+
+#include <cstdio>
+#include <cassert>
+#include <vector>
+#include <list>
+#include <set>
+#include <string>
+#include <queue>
+
+#include <CGAL/bounding_box.h>
+#include <CGAL/Search_traits_3.h>
+#include <CGAL/Fuzzy_sphere.h>
+#include <CGAL/Orthogonal_k_neighbor_search.h>
+#include <CGAL/Default_diagonalize_traits.h>
+#include <CGAL/centroid.h>
+#include <CGAL/compute_average_spacing.h>
+#include <CGAL/linear_least_squares_fitting_3.h>
+
+#include <CGAL/Data_classification/Image.h>
+#include <CGAL/Data_classification/Color.h>
+#include <CGAL/Data_classification/Segmentation_attribute.h>
+#include <CGAL/gco/GCoptimization.h>
+#include <boost/iterator/counting_iterator.hpp>
+
+#define CGAL_CLASSIFICATION_VERBOSE
+#if defined(CGAL_CLASSIFICATION_VERBOSE)
+#define CGAL_CLASSIFICATION_CERR std::cerr
+#else
+#define CGAL_CLASSIFICATION_CERR std::ostream(0)
+#endif
+
+namespace CGAL {
+
+
+
+/*!
+\ingroup PkgDataClassification
+
+\brief Definition of a classification type based on an ID and a set of
+relationship with attributes.
+
+A classification type is used to segment the input data set. Usual
+classification types are ground, vegetation and buildings (but other
+can be defined).
+
+*/
+class Classification_type
+{
+public:
+  
+  enum Attribute_side /// Defines the effect of the values of an attribute on the classification type.
+    {
+      FAVORED_ATT = 0, ///< High values of the attribute favor this type
+      NEUTRAL_ATT = 1, ///< The attribute has no effect on this type
+      PENALIZED_ATT = 2 ///< Low values of the attribute favor this type
+    };
+
+private:
+  std::string m_id;
+  std::map<Segmentation_attribute*, Attribute_side> m_attribute_effects;
+
+public:
+
+  /// \name Main methods 
+  /// @{
+  /*! 
+    \param id The name of the classification type
+    (e.g. vegetation). Two different classification types must have
+    different IDs.
+  */ 
+  Classification_type (std::string id) : m_id (id) { }
+
+  /*! 
+    \brief Sets how an attribute affects the classification type.
+
+    \param att Attribute whose effect on the classification type will be set
+
+    \param effect The effect the attribute will have on the classification type
+
+  */ 
+  void set_attribute_effect (Segmentation_attribute* att, Attribute_side effect)
+  {
+    m_attribute_effects[att] = effect;
+  }
+
+  /*!
+    \brief Get the effects of an attribute on the classification type.
+
+    \param att Attribute
+
+    \return The effect of the attribute on the classification type.
+   */
+  Attribute_side attribute_effect (Segmentation_attribute* att) 
+  {
+    std::map<Segmentation_attribute*, Attribute_side>::iterator
+      search = m_attribute_effects.find (att);
+    return (search == m_attribute_effects.end () ? NEUTRAL_ATT : search->second);
+  }
+
+  /*!
+    \brief Get the ID of the classification type.
+
+    \return The ID of the classification type.
+  */
+  const std::string& id() const { return m_id; }
+
+  /// @}
+  
+  /// \cond SKIP_IN_MANUAL
+  void info()
+  {
+    std::cerr << "Attribute " << m_id << ": ";
+    for (std::map<Segmentation_attribute*, Attribute_side>::iterator it = m_attribute_effects.begin();
+         it != m_attribute_effects.end(); ++ it)
+      {
+        if (it->second == NEUTRAL_ATT)
+          continue;
+        
+        std::cerr << it->first;
+        if (it->second == FAVORED_ATT) std::cerr << " (favored), ";
+        else if (it->second == PENALIZED_ATT) std::cerr << " (penalized), ";
+      }
+    std::cerr << std::endl;
+  }
+  /// \endcond
+
+};
+
+
+
+/*!
+\ingroup PkgDataClassification
+
+\brief Classifies a point set based on a set of attribute and a set of classification types.
+
+This class implement the core of the algorithm. It uses a point set as
+input. Based on a set of segmentation attributes and a set of
+classification types, it segments the point set into the different
+types given. The output can be regularized with different smoothing
+methods.
+
+\tparam Kernel The geometric kernel used
+
+*/
+template <typename Kernel>
+class Point_set_classification
+{
+
+  
+public:
+  /// \cond SKIP_IN_MANUAL
+  typedef typename Kernel::Point_3 Point;
+  typedef typename Kernel::Segment_3 Segment;
+  typedef typename Kernel::FT FT;
+  typedef typename Kernel::Line_3 Line;
+  typedef typename Kernel::Plane_3 Plane;
+  typedef typename Kernel::Vector_3 Vector;
+
+  typedef CGAL::cpp11::array<double, 3> Eigenvalues;
+  
+  typedef typename Kernel::Iso_cuboid_3 Iso_cuboid_3;
+
+  typedef Data_classification::Image<std::vector<int> > Image_indices;
+  typedef Data_classification::Image<bool> Image_bool;
+  typedef Data_classification::Image<float> Image_float;
+  typedef Data_classification::RGB_Color RGB_Color;
+  typedef Data_classification::HSV_Color HSV_Color;
+  
+  struct HPoint {
+    Point position;
+    unsigned char echo; 
+    int ind_x;
+    int ind_y;
+    std::size_t group; 
+    unsigned char AE_label;
+    unsigned char neighbor;
+    double confidence;
+    RGB_Color color;
+  };
+
+  struct Cluster
+  {
+    Point centroid;
+    std::vector<std::size_t> indices;
+    std::set<std::size_t> neighbors;
+  };
+
+  
+  class My_point_property_map{
+    const std::vector<HPoint>& points;
+  public:
+    typedef Point value_type;
+    typedef const value_type& reference;
+    typedef std::size_t key_type;
+    typedef boost::lvalue_property_map_tag category;  
+    My_point_property_map (const std::vector<HPoint>& pts) : points (pts) {}
+    reference operator[] (key_type k) const { return points[k].position; }
+    friend inline reference get (const My_point_property_map& ppmap, key_type i) 
+    { return ppmap[i]; }
+  };
+  
+  typedef CGAL::Search_traits_3<Kernel> SearchTraits_3;
+  typedef Search_traits_adapter <std::size_t, My_point_property_map, SearchTraits_3> Search_traits;
+  typedef CGAL::Kd_tree<Search_traits> Tree;
+  typedef CGAL::Fuzzy_sphere<Search_traits> Fuzzy_sphere;
+  typedef CGAL::Orthogonal_k_neighbor_search<Search_traits> Neighbor_search;
+  typedef typename Neighbor_search::Tree KTree;
+  typedef typename Neighbor_search::Distance Distance;
+
+  std::vector<Classification_type*> segmentation_classes; 
+  std::vector<Segmentation_attribute*> segmentation_attributes; 
+
+  typedef Classification_type::Attribute_side Attribute_side;
+  std::vector<std::vector<Attribute_side> > effect_table;
+
+  bool has_colors;
+  bool is_echo_given;
+  Iso_cuboid_3 BBox_scan;
+
+  std::vector<HPoint> HPS;
+  Image_indices grid_HPS;
+
+  //Hpoints attributes
+  std::vector<Eigenvalues> eigenvalues;
+  std::vector<Plane> planes;
+
+  std::vector<Plane> groups;
+  std::vector<Cluster> clusters;
+  
+  Image_bool Mask;                     //imagg
+  Image_float DTM; //a enregistrer ?   //imagg           
+
+  double m_grid_resolution;
+  double m_radius_neighbors; 
+  double m_radius_dtm;
+  bool m_multiplicative;
+  /// \endcond
+
+  /// \name Main methods
+  /// @{
+  
+  /*! 
+    \brief Constructs a classification object based on the input range.
+
+    \tparam InputIterator Iterator on the input point. Value type must be `Point_3<Kernel>`.
+
+    \param begin Iterator to the first input point
+
+    \param end Past-the-end iterator
+
+    \param grid_resolution Resolution of the 2D map of the ground. If
+    the default value is used, it is computed as the average spacing
+    of the point set.
+
+    \param radius_neighbors Size used for neighborhood computation. If
+    the default value is used, it is computed as 5 times
+    `grid_resolution`.
+
+    \param radius_dtm Size used to estimate the ground (should be
+    higher than the thickest non-ground object of the scene). If the
+    default value is used, it is computed as 5 times `radius_neighbors`.
+  */ 
+  template <typename InputIterator>
+  Point_set_classification (InputIterator begin, InputIterator end,
+                            double grid_resolution = -1.,
+                            double radius_neighbors = -1.,
+                            double radius_dtm = -1.)
+    : m_grid_resolution (grid_resolution),
+      m_radius_neighbors (radius_neighbors),
+      m_radius_dtm (radius_dtm)
+  {
+    if (m_grid_resolution < 0.)
+      m_grid_resolution = CGAL::compute_average_spacing<CGAL::Sequential_tag> (begin, end, 6);
+    if (m_radius_neighbors < 0.)
+      m_radius_neighbors = 5. * m_grid_resolution;
+    if (m_radius_dtm < 0.)
+      m_radius_dtm = 5 * m_grid_resolution;
+
+    for (InputIterator it = begin; it != end; ++ it)
+      {
+        HPS.push_back (HPoint());
+        HPS.back().position = *it;
+        HPS.back().echo = -1;
+        HPS.back().ind_x = -1;
+        HPS.back().ind_y = -1;
+        HPS.back().group = (std::size_t)(-1);
+        HPS.back().AE_label = (unsigned char)(-1);
+        HPS.back().neighbor = (unsigned char)(-1);
+        HPS.back().confidence = 0;
+        RGB_Color c = {{ 0, 0, 0 }};
+        HPS.back().color = c;
+      }
+    has_colors = false;
+    is_echo_given = false;
+    m_multiplicative = false;
+  }
+
+  /// \cond SKIP_IN_MANUAL
+  void change_hue (RGB_Color& color, const RGB_Color& hue)
+  {
+    HSV_Color hcolor = Data_classification::rgb_to_hsv (color);
+    HSV_Color hhue = Data_classification::rgb_to_hsv (hue);
+    hcolor[0] = hhue[0];
+    //    hcolor[1] = hhue[1];
+    color = Data_classification::hsv_to_rgb (hcolor);
+  }
+  /// \endcond
+
+  /*!
+    \brief Change the parameters of the classification algorithm.
+
+    \param grid_resolution Resolution of the 2D map of the ground. 
+
+    \param radius_neighbors Size used for neighborhood computation. 
+
+    \param radius_dtm Size used to estimate the ground (should be
+    higher than the thickest non-ground object of the scene). 
+  */
+  
+  void set_parameters (double grid_resolution, double radius_neighbors, double radius_dtm)
+  {
+    m_grid_resolution = grid_resolution;
+    m_radius_neighbors = radius_neighbors;
+    m_radius_dtm = radius_dtm;
+  }
+
+  /*!
+    Computes all the interlate structures needed by the algorithm.
+   */
+  void initialization()
+  {
+    clock_t t;
+    t = clock();
+
+    CGAL_CLASSIFICATION_CERR << std::endl << "Initialization: ";
+
+    //1-Neighborhood computation and reset the attributes of the structure points
+    CGAL_CLASSIFICATION_CERR<<"spherical neighborhood..";
+    eigenvalues.clear();
+    planes.clear();
+    
+    std::vector<Point> list_points;
+    for(int i=0;i<(int)HPS.size();i++){
+      Point pt=HPS[i].position;
+      list_points.push_back(pt);
+    }
+    
+    My_point_property_map pmap (HPS);
+    Tree tree (boost::counting_iterator<std::size_t> (0),
+               boost::counting_iterator<std::size_t> (HPS.size()),
+               typename Tree::Splitter(),
+               Search_traits (pmap));
+
+    std::size_t nb_neigh = 0;
+    for(int i=0;i<(int)HPS.size();i++){
+      const Point& query=HPS[i].position;
+      std::vector<std::size_t> neighbors;
+      
+      Fuzzy_sphere fs(i, m_radius_neighbors, 0, tree.traits());
+      tree.search(std::back_inserter(neighbors), fs);
+      nb_neigh += neighbors.size();
+      std::vector<Point> neighborhood;
+      for (std::size_t j = 0; j < neighbors.size(); ++ j)
+        neighborhood.push_back (HPS[neighbors[j]].position);
+        
+      compute_principal_curvature (query, neighborhood);
+    }
+
+    CGAL_CLASSIFICATION_CERR<<"ok";
+
+    //2-creation of the bounding box
+    CGAL_CLASSIFICATION_CERR<<", bounding box..";
+    BBox_scan = CGAL::bounding_box(list_points.begin(), list_points.end());
+    CGAL_CLASSIFICATION_CERR<<"ok";
+
+    //3-creation grille_points
+    CGAL_CLASSIFICATION_CERR<<", planimetric grid of HPS..";
+    Image_indices tess((std::size_t)((BBox_scan.xmax()-BBox_scan.xmin())/m_grid_resolution)+1,
+                       (std::size_t)((BBox_scan.ymax()-BBox_scan.ymin())/m_grid_resolution)+1);
+    grid_HPS=tess;
+
+    for(int i=0;i<(int)HPS.size();i++){
+
+      //for each 3D point, its coordinates in the grid are inserted in its Hpoint structure
+      HPS[i].ind_x=(int)((HPS[i].position.x()-BBox_scan.xmin())/m_grid_resolution);
+      HPS[i].ind_y=(int)((HPS[i].position.y()-BBox_scan.ymin())/m_grid_resolution);
+
+      //index of points are collected in grid_HPS
+      std::vector < int > temp;
+      temp=grid_HPS(HPS[i].ind_x,HPS[i].ind_y);
+      temp.push_back(i);
+      grid_HPS(HPS[i].ind_x,HPS[i].ind_y)=temp;
+    }
+    CGAL_CLASSIFICATION_CERR<<"ok";
+
+
+    //4-Mask creation
+    CGAL_CLASSIFICATION_CERR<<", planimetric mask..";
+    Image_bool masktp((std::size_t)((BBox_scan.xmax()-BBox_scan.xmin())/m_grid_resolution)+1,
+                      (std::size_t)((BBox_scan.ymax()-BBox_scan.ymin())/m_grid_resolution)+1);
+    Mask=masktp;
+
+    CGAL_CLASSIFICATION_CERR << "(" << Mask.height() << "x" << Mask.width() << ")" << std::endl;
+    int square=(int)16*(m_radius_neighbors/m_grid_resolution)+1;
+    int nb_true = 0;
+    for (int j=0;j<(int)Mask.height();j++){	
+      for (int i=0;i<(int)Mask.width();i++){	
+        //Mask(i,j)=false;
+        if(grid_HPS(i,j).size()>0) { Mask(i,j)=true; nb_true ++; }
+        else{Mask(i,j)=false;}
+      }
+    }
+
+    Image_bool Mask_tmp ((std::size_t)((BBox_scan.xmax()-BBox_scan.xmin())/m_grid_resolution)+1,
+                         (std::size_t)((BBox_scan.ymax()-BBox_scan.ymin())/m_grid_resolution)+1);
+
+    for (std::size_t i = 0; i < Mask.width(); ++ i)
+      for (std::size_t j = 0; j < Mask.height(); ++ j)
+        {
+          if(!Mask(i,j))
+            {
+              int squareYmin=std::max(0,(int)j-square);
+              int squareYmax=std::min((int)(Mask.height())-1,(int)j+square);
+
+              for (int k = squareYmin; k <= squareYmax; ++ k)
+                if (Mask(i,k))
+                  {
+                    Mask_tmp(i,j) = true;
+                    break;
+                  }
+            }
+          else
+            Mask_tmp(i,j) = true;
+        }
+
+    for (std::size_t i = 0; i < Mask.width(); ++ i)
+      for (std::size_t j = 0; j < Mask.height(); ++ j)
+        {
+          if(!Mask_tmp(i,j))
+            {
+              int squareXmin=std::max(0,(int)i-square);
+              int squareXmax=std::min((int)(Mask.width())-1,(int)i+square);
+
+              for (int k = squareXmin; k <= squareXmax; ++ k)
+                if (Mask_tmp(k,j))
+                  {
+                    Mask(i,j) = true;
+                    break;
+                  }
+            }
+          else
+            Mask(i,j) = true;
+        }
+
+    CGAL_CLASSIFICATION_CERR<<std::endl<<"-> OK ( "<<((float)clock()-t)/CLOCKS_PER_SEC<<" sec )"<< std::endl;
+  }
+
+
+  /// \cond SKIP_IN_MANUAL
+  void compute_principal_curvature(const Point& point, std::vector<Point>& neighborhood)
+  {
+    if (neighborhood.size() == 0)
+      {
+        Eigenvalues v = {{ 0., 0., 0. }};
+        eigenvalues.push_back (v);
+        planes.push_back (Plane (point, Vector (0., 0., 1.)));
+        return;
+      }
+    Point centroid = CGAL::centroid (neighborhood.begin(), neighborhood.end());
+
+    CGAL::cpp11::array<double, 6> covariance = {{ 0., 0., 0., 0., 0., 0. }};
+      
+    for (std::size_t i = 0; i < neighborhood.size(); ++ i)
+      {
+        Vector d = neighborhood[i] - centroid;
+        covariance[0] += d.x () * d.x ();
+        covariance[1] += d.x () * d.y ();
+        covariance[2] += d.x () * d.z ();
+        covariance[3] += d.y () * d.y ();
+        covariance[4] += d.y () * d.z ();
+        covariance[5] += d.z () * d.z ();
+      }
+
+    Eigenvalues evalues = {{ 0., 0., 0. }};
+    CGAL::cpp11::array<double, 9> eigenvectors = {{ 0., 0., 0.,
+                                                    0., 0., 0.,
+                                                    0., 0., 0. }};
+
+    CGAL::Default_diagonalize_traits<double,3>::diagonalize_selfadjoint_covariance_matrix
+      (covariance, evalues, eigenvectors);
+    eigenvalues.push_back (evalues);
+
+    Plane plane;
+    CGAL::linear_least_squares_fitting_3 (neighborhood.begin(),neighborhood.end(),plane, CGAL::Dimension_tag<0>());
+    planes.push_back (plane);
+  }
+
+  double classification_value (std::size_t segmentation_class, int pt_index) const
+  {
+    double out = 0.;
+    if (m_multiplicative)
+      {
+        out = 1.;
+        for (std::size_t i = 0; i < effect_table[segmentation_class].size(); ++ i)
+          {
+            if (effect_table[segmentation_class][i] == Classification_type::FAVORED_ATT)
+              out *= segmentation_attributes[i]->favored (pt_index);
+            else if (effect_table[segmentation_class][i] == Classification_type::PENALIZED_ATT)
+              out *= segmentation_attributes[i]->penalized (pt_index);
+            else if (effect_table[segmentation_class][i] == Classification_type::NEUTRAL_ATT)
+              out *= segmentation_attributes[i]->ignored (pt_index);
+          }
+      }
+    else
+      {
+        for (std::size_t i = 0; i < effect_table[segmentation_class].size(); ++ i)
+          {
+            if (effect_table[segmentation_class][i] == Classification_type::FAVORED_ATT)
+              out += segmentation_attributes[i]->favored (pt_index);
+            else if (effect_table[segmentation_class][i] == Classification_type::PENALIZED_ATT)
+              out += segmentation_attributes[i]->penalized (pt_index);
+            else if (effect_table[segmentation_class][i] == Classification_type::NEUTRAL_ATT)
+              out += segmentation_attributes[i]->ignored (pt_index);
+          }
+      }
+    return out;
+  }
+
+
+  bool quick_labeling_PC()
+  {
+
+    // data term initialisation
+    CGAL_CLASSIFICATION_CERR << "Labeling... ";
+
+
+    int count1 = 0, count2 = 0, count3 = 0, count4 = 0;
+    for(int s=0;s<(int)HPS.size();s++){
+			
+      int nb_class_best=0; 
+
+      double val_class_best = std::numeric_limits<double>::max();
+      std::vector<double> values;
+      
+      for(std::size_t k = 0; k < effect_table.size(); ++ k)
+        {
+          double value = classification_value (k, s);
+          values.push_back (value);
+          
+          if(val_class_best > value)
+            {
+              val_class_best = value;
+              nb_class_best=k;
+            }
+        }
+
+      HPS[s].AE_label = nb_class_best;
+
+      std::sort (values.begin(), values.end());
+      if (m_multiplicative)
+        HPS[s].confidence = (values[1] - values[0]) / values[1];
+      else
+        HPS[s].confidence = values[1] - values[0];
+
+      if(nb_class_best==0) count1++;
+      else if(nb_class_best==1) count2++;
+      else if(nb_class_best==2) count3++;
+      else count4++;
+
+    }
+    
+    CGAL_CLASSIFICATION_CERR<<" "<<(double)100*count1/HPS.size()<<"% vegetation, "<<(double)100*count2/HPS.size()<<"% ground, "<<(double)100*count3/HPS.size()<<"% roof, "<<(double)100*count4/HPS.size()<<"% clutter"<<std::endl;
+	
+    return true;
+  }
+
+
+  bool smoothed_labeling_PC()
+  {
+
+    // data term initialisation
+    CGAL_CLASSIFICATION_CERR << "Labeling... ";
+
+    std::vector<std::vector<double> > values
+      (segmentation_classes.size(),
+       std::vector<double> (HPS.size(), -1.));
+
+    My_point_property_map pmap (HPS);
+
+    Tree tree (boost::counting_iterator<std::size_t> (0),
+               boost::counting_iterator<std::size_t> (HPS.size()),
+               typename Tree::Splitter(),
+               Search_traits (pmap));
+
+    for (std::size_t s=0; s < HPS.size(); ++ s)
+      {
+        std::vector<std::size_t> neighbors;
+      
+        Fuzzy_sphere fs(s, m_radius_neighbors, 0, tree.traits());
+        tree.search(std::back_inserter(neighbors), fs);
+
+        std::vector<double> mean (values.size(), 0.);
+        for (std::size_t n = 0; n < neighbors.size(); ++ n)
+          {
+            if (values[0][neighbors[n]] < 0.)
+              for(std::size_t k = 0; k < effect_table.size(); ++ k)
+                {
+                  values[k][neighbors[n]] = classification_value (k, neighbors[n]);
+                  mean[k] += values[k][neighbors[n]];
+                }
+            else
+              for (std::size_t j = 0; j < values.size(); ++ j)
+                mean[j] += values[j][neighbors[n]];
+          }
+
+        int nb_class_best=0; 
+        double val_class_best = std::numeric_limits<double>::max();
+        for(std::size_t k = 0; k < mean.size(); ++ k)
+          {
+            mean[k] /= neighbors.size();
+            if(val_class_best > mean[k])
+              {
+                val_class_best = mean[k];
+                nb_class_best = k;
+              }
+          }
+
+        HPS[s].AE_label = nb_class_best;
+
+        std::sort (mean.begin(), mean.end());
+        HPS[s].confidence = mean[1] - mean[0];      
+
+      }
+
+	
+    return true;
+  }
+
+
+  class Edge_score
+  {
+    const Point_set_classification& M;
+
+  public:
+    Edge_score(const Point_set_classification& _M) : M(_M) {}
+
+    float compute(int, int, int l1, int l2)
+    {
+      double res=0;
+      double smooth_seg= M.m_grid_resolution;
+
+      if(l1!=l2) res=1; 
+
+      return smooth_seg*res;
+    }
+  };
+
+
+  class Facet_score
+  {
+    const Point_set_classification& M;
+
+  public:
+    Facet_score(const Point_set_classification& _M) : M(_M) {}
+
+    double compute(int s, int l)
+    {
+      return M.classification_value (l, s);
+    }
+  };
+  
+  bool graphcut_labeling_PC()
+  {
+
+    std::size_t nb_alpha_exp = 2;
+    
+    // data term initialisation
+    CGAL_CLASSIFICATION_CERR << "Labeling... ";
+
+    std::vector<std::vector<double> > values
+      (segmentation_classes.size(),
+       std::vector<double> (HPS.size(), -1.));
+
+    My_point_property_map pmap (HPS);
+
+    KTree tree (boost::counting_iterator<std::size_t> (0),
+                boost::counting_iterator<std::size_t> (HPS.size()),
+                typename KTree::Splitter(),
+                Search_traits (pmap));
+
+    GCoptimizationGeneralGraph<float, float> *gc= new GCoptimizationGeneralGraph<float, float>
+      ((int)HPS.size(),(int)(segmentation_classes.size()));
+
+    gc->specializeDataCostFunctor(Facet_score(*this));
+    gc->specializeSmoothCostFunctor(Edge_score(*this));
+
+    Distance tr_dist(pmap);
+    for (std::size_t s=0; s < HPS.size(); ++ s)
+      {
+        std::vector<std::size_t> neighbors;
+
+        Neighbor_search search (tree, HPS[s].position, 12, 0, true, tr_dist);
+        for (typename Neighbor_search::iterator it = search.begin(); it != search.end(); ++ it)
+          gc->setNeighbors (s, it->first);
+        
+        int nb_class_best=0; 
+        double val_class_best = std::numeric_limits<double>::max();
+        for(std::size_t k = 0; k < effect_table.size(); ++ k)
+          {
+            double value = classification_value (k, s);
+
+            if(val_class_best > value)
+              {
+                val_class_best = value;
+                nb_class_best=k;
+              }
+          }
+        gc->setLabel (s, nb_class_best);
+
+      }
+
+    CGAL_CLASSIFICATION_CERR << "Graph cut... ";
+    for (std::size_t iter = 0; iter < nb_alpha_exp; ++ iter)
+      {
+        for (std::size_t i = 0; i< segmentation_classes.size(); ++ i)
+          {
+				
+            // Compute vector of active sites
+            std::vector<int> sites;
+            for (std::size_t s = 0; s < HPS.size(); ++ s)
+              sites.push_back ((int)s);
+            
+            // Compute alpha expansion for this label on these sites
+            gc->alpha_expansion((int)i, &(sites[0]), sites.size());
+          }
+      }
+    
+    for (std::size_t s=0; s < HPS.size(); ++ s)
+      HPS[s].AE_label = gc->whatLabel (s);
+
+    delete gc;
+      
+    return true;
+  }
+  
+  void reset_groups()
+  {
+    groups.clear();
+    for (std::size_t i = 0; i < HPS.size(); ++ i)
+      HPS[i].group = (std::size_t)(-1);
+  }
+
+  void cluster_points (const double& tolerance)
+  {
+    if (clusters.empty())
+      {
+        My_point_property_map pmap (HPS);
+
+        Tree tree (boost::counting_iterator<std::size_t> (0),
+                   boost::counting_iterator<std::size_t> (HPS.size()),
+                   typename Tree::Splitter(),
+                   Search_traits (pmap));
+
+        std::vector<std::size_t> done (HPS.size(), (std::size_t)(-1));
+
+        for (std::size_t s=0; s < HPS.size(); ++ s)
+          HPS[s].neighbor = (unsigned char)(-1);
+    
+        for (std::size_t s=0; s < HPS.size(); ++ s)
+          {
+            if (done[s] != (std::size_t)(-1))
+              continue;
+            unsigned char label = HPS[s].AE_label;
+        
+            clusters.push_back (Cluster());
+
+            std::queue<std::size_t> todo;
+            todo.push (s);
+            done[s] = clusters.size()-1;
+
+            while (!(todo.empty()))
+              {
+                std::size_t current = todo.front();
+                todo.pop();
+                clusters.back().indices.push_back (current);
+            
+                std::vector<std::size_t> neighbors;
+                Fuzzy_sphere fs(current, tolerance, 0, tree.traits());
+                tree.search(std::back_inserter(neighbors), fs);
+            
+                for (std::size_t n = 0; n < neighbors.size(); ++ n)
+                  {
+                    if (done[neighbors[n]] == (std::size_t)(-1))
+                      {
+                        if (HPS[neighbors[n]].AE_label == label)
+                          {
+                            todo.push (neighbors[n]);
+                            done[neighbors[n]] = clusters.size()-1;
+                          }
+                        else
+                          {
+                            HPS[current].neighbor = HPS[neighbors[n]].AE_label;
+                            HPS[neighbors[n]].neighbor = HPS[current].AE_label;
+                            continue;
+                          }
+                      }
+                    else if (done[neighbors[n]] != clusters.size()-1)
+                      {
+                        clusters.back().neighbors.insert (done[neighbors[n]]);
+                        clusters[done[neighbors[n]]].neighbors.insert (clusters.size()-1);
+                      }
+                  }
+              }
+          }
+        std::cerr << "Found " << clusters.size() << " cluster(s)" << std::endl;
+
+        for (std::size_t i = 0; i < clusters.size(); ++ i)
+          {
+            std::vector<Point> pts;
+            for (std::size_t j = 0; j < clusters[i].indices.size(); ++ j)
+              pts.push_back (HPS[clusters[i].indices[j]].position);
+            clusters[i].centroid = CGAL::centroid (pts.begin(), pts.end());
+        
+          }
+      }
+    else
+      {
+        std::size_t index_ground = 0;
+        std::size_t index_roof = 0;
+        std::size_t index_facade = 0;
+        std::size_t index_vege = 0;
+  
+        for (std::size_t i = 0; i < segmentation_classes.size(); ++ i)
+          if (segmentation_classes[i]->id() == "ground")
+            index_ground = i;
+          else if (segmentation_classes[i]->id() == "roof")
+            index_roof = i;
+          else if (segmentation_classes[i]->id() == "facade")
+            index_facade = i;
+          else if (segmentation_classes[i]->id() == "vegetation")
+            index_vege = i;
+
+        std::size_t min_ind = 0;
+        double min = 1.;
+        
+        for (std::size_t i = 0; i < clusters.size(); ++ i)
+          {
+            std::size_t nb_border = 0;
+            std::size_t nb_good = 0;
+
+            for (std::size_t n = 0; n < clusters[i].indices.size(); ++ n)
+              {
+                if (HPS[clusters[i].indices[n]].neighbor == (unsigned char)(-1))
+                  continue;
+
+                std::size_t c0 = HPS[clusters[i].indices[n]].AE_label;
+                std::size_t c1 = HPS[clusters[i].indices[n]].neighbor;
+                nb_border ++;
+                if ((c0 == index_ground && c1 == index_facade) || (c0 == index_facade && c1 == index_ground) ||
+                    (c0 == index_ground && c1 == index_vege) || (c0 == index_vege && c1 == index_ground) ||
+                    (c0 == index_facade && c1 == index_roof) || (c0 == index_roof && c1 == index_facade))
+                  nb_good ++;
+              }
+
+            if (nb_border == 0)
+              continue;
+
+            double ratio = nb_good / (double)nb_border;
+            if (ratio < min)
+              {
+                min = ratio;
+                min_ind = i;
+              }
+          }
+
+        std::cerr << "Found cluster " << min_ind << " with only " << (int)(100. * min) << "% of correct border." << std::endl;
+
+        std::vector<double> values (segmentation_classes.size(), 0.);
+        
+        for (std::size_t n = 0; n < clusters[min_ind].indices.size(); ++ n)
+          {
+            for (std::size_t i = 0; i < values.size(); ++ i)
+              values[i] += classification_value (i, clusters[min_ind].indices[n]);
+          }
+
+        std::size_t nb_class_best = 0;
+        double val_class_best = std::numeric_limits<double>::max();
+        
+        for (std::size_t i = 0; i < values.size(); ++ i)
+          {
+            if (i == HPS[clusters[min_ind].indices[0]].AE_label)
+              continue;
+
+            if (values[i] < val_class_best)
+              {
+                nb_class_best = i;
+                val_class_best = values[i];
+              }
+          }
+
+        for (std::size_t n = 0; n < clusters[min_ind].indices.size(); ++ n)
+          HPS[clusters[min_ind].indices[n]].AE_label = nb_class_best;
+        
+        clusters.clear();        
+      }
+
+  }
+
+  /// \cond SKIP_IN_MANUAL
+  bool regularized_labeling_PC()
+  {
+    std::vector<std::vector<std::size_t> > groups;
+    for (std::size_t i = 0; i < HPS.size(); ++ i)
+      {
+        std::size_t index = HPS[i].group;
+        if (index == (std::size_t)(-1))
+          continue;
+
+        if (groups.size() <= index)
+          groups.resize (index + 1);
+        
+        groups[index].push_back (i);
+      }
+
+    if (groups.empty())
+      return false;
+        
+    // data term initialisation
+    CGAL_CLASSIFICATION_CERR << "Labeling... ";
+
+    std::vector<std::vector<double> > values;
+    values.resize (segmentation_classes.size());
+    
+    std::map<Point, std::size_t> map_p2i;
+    for(int s=0;s<(int)HPS.size();s++)
+      {
+        map_p2i[HPS[s].position] = s;
+
+        int nb_class_best=0; 
+        double val_class_best = std::numeric_limits<double>::max();
+        for(std::size_t k = 0; k < effect_table.size(); ++ k)
+          {
+            double v = classification_value (k, s);
+
+            values[k].push_back(v);
+            if (v < val_class_best)
+              {
+                nb_class_best = k;
+                val_class_best = v;
+              }
+          }
+        HPS[s].AE_label = nb_class_best;
+      }
+
+    for(std::size_t i = 0; i < groups.size(); ++ i)
+      {
+        std::vector<double> mean (values.size(), 0.);
+
+        for (std::size_t n = 0; n < groups[i].size(); ++ n)
+          {
+            for (std::size_t j = 0; j < values.size(); ++ j)
+              mean[j] += values[j][groups[i][n]];
+          }
+        
+        int nb_class_best=0; 
+
+        double val_class_best = std::numeric_limits<double>::max();
+
+        for (std::size_t j = 0; j < mean.size(); ++ j)
+          if (mean[j] < val_class_best)
+            {
+              nb_class_best = j;
+              val_class_best = mean[j];
+            }
+
+        for (std::size_t n = 0; n < groups[i].size(); ++ n)
+          {
+            HPS[groups[i][n]].AE_label = nb_class_best;
+            for (std::size_t j = 0; j < mean.size(); ++ j)
+              values[j][groups[i][n]] = mean[j] / groups[i].size();
+          }
+      }
+
+    My_point_property_map pmap (HPS);
+
+    Tree tree (boost::counting_iterator<std::size_t> (0),
+               boost::counting_iterator<std::size_t> (HPS.size()),
+               typename Tree::Splitter(),
+               Search_traits (pmap));
+
+    for (std::size_t s=0; s < HPS.size(); ++ s)
+      {
+        if (HPS[s].group != (std::size_t)(-1))
+          continue;
+        
+        std::vector<std::size_t> neighbors;
+      
+        Fuzzy_sphere fs(s, m_radius_neighbors, 0, tree.traits());
+        tree.search(std::back_inserter(neighbors), fs);
+
+        std::vector<double> mean (values.size(), 0.);
+        for (std::size_t n = 0; n < neighbors.size(); ++ n)
+          for (std::size_t j = 0; j < values.size(); ++ j)
+            mean[j] += values[j][neighbors[n]];
+
+        int nb_class_best=0; 
+        double val_class_best = std::numeric_limits<double>::max();
+        for(std::size_t k = 0; k < mean.size(); ++ k)
+          {
+            mean[k] /= neighbors.size();
+            if(val_class_best > mean[k])
+              {
+                val_class_best = mean[k];
+                nb_class_best = k;
+              }
+          }
+
+        HPS[s].AE_label = nb_class_best;
+
+        std::sort (mean.begin(), mean.end());
+        HPS[s].confidence = mean[1] - mean[0];      
+
+      }
+    
+	
+    return true;
+  }
+  /// \endcond
+
+  /*!
+    Performs classification using the user-chosen regularization method.
+
+    \param method Regularization method. 0 = no regularization; 1 =
+    global regularization through graphcut; 2 = local regularization
+    based on pre-computed groups.
+
+    \note Classification without regularization (method = 0) is very
+    quick: almost instantaneous or up to a few seconds for very large
+    point clouds. Regularization improves the quality of the output at
+    the cost of longer computation time.
+  */
+  void classify (int method)
+  {
+
+    clock_t t;
+    t = clock();
+
+    build_effect_table ();
+	
+    CGAL_CLASSIFICATION_CERR<<std::endl<<"Classification of the point cloud: ";
+    
+    if (method == 0)
+      quick_labeling_PC();
+    else if (method == 1)
+      //      smoothed_labeling_PC();
+      graphcut_labeling_PC();
+    else if (method == 2)
+      regularized_labeling_PC();
+    else
+      {
+        std::cerr << "Unknown method number." << std::endl;
+        abort();
+      }
+
+    CGAL_CLASSIFICATION_CERR<<"-> OK ( "<<((float)clock()-t)/CLOCKS_PER_SEC<<" sec )"<< std::endl;
+
+  }
+
+  /// @}
+
+  
+  /// \cond SKIP_IN_MANUAL
+  void build_effect_table ()
+  {
+    effect_table = std::vector<std::vector<Attribute_side> >
+      (segmentation_classes.size(), std::vector<Attribute_side> (segmentation_attributes.size(),
+                                                                 Classification_type::NEUTRAL_ATT));
+    
+    for (std::size_t i = 0; i < effect_table.size (); ++ i)
+      for (std::size_t j = 0; j < effect_table[i].size (); ++ j)
+        effect_table[i][j] = segmentation_classes[i]->attribute_effect (segmentation_attributes[j]);
+
+  }
+  /// \endcond
+
+
+
+#ifdef DOXYGEN_RUNNING
+
+  /// \name Types and attributes
+  /// @{
+  
+  /*!
+    \brief Add a classification type
+    \param type Pointer to the classification type object
+   */
+  void add_classification_type (Classification_type* type);
+
+  void clear_classification_types ();
+
+  /*!
+    \brief Add a segmentation attribute
+    \param attribute Pointer to the attribute object
+   */
+  void add_segmentation_attribute (Segmentation_attribute* attribute);
+
+  void clear_segmentation_attributes ();
+  
+  /// @}
+
+  /// \name Groups
+  /// @{
+
+  /*!
+    \brief Add a point to a group
+
+    Grouping points can be used for regularization (for example, to
+    apply the same classification type to all inliers of a detected
+    RANSAC primitive).
+
+    \param point_index Index of the point in the input range
+    \param group_index Index of the group
+   */
+  void add_to_group (std::size_t point_index, std::size_t group_index);
+
+  /*!
+    \brief Reset all groups attributes of points
+   */
+  void clear_groups();
+
+  /// @}
+  
+
+  /// \name Output
+  /// @{
+
+  /*!
+    \brief Get the classification type of indexed point.
+    \param index Index of the input point
+    \return Pointer to the classification type 
+  */
+  Classification_type* classification_type_of (std::size_t index) const;
+
+  /// @}
+
+#endif
+
+
+};
+
+
+
+
+
+} // namespace CGAL
+
+#endif // CGAL_POINT_SET_CLASSIFICATION_H
+
diff --git a/Data_classification/package_info/Data_classification/copyright.txt b/Data_classification/package_info/Data_classification/copyright.txt
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/Data_classification/package_info/Data_classification/description.txt b/Data_classification/package_info/Data_classification/description.txt
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/Data_classification/package_info/Data_classification/license.txt b/Data_classification/package_info/Data_classification/license.txt
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/Data_classification/package_info/Data_classification/long_description.txt b/Data_classification/package_info/Data_classification/long_description.txt
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/Data_classification/package_info/Data_classification/maintainer b/Data_classification/package_info/Data_classification/maintainer
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/Documentation/doc/Documentation/dependencies b/Documentation/doc/Documentation/dependencies
index a3ea72f209c..66e1681d910 100644
--- a/Documentation/doc/Documentation/dependencies
+++ b/Documentation/doc/Documentation/dependencies
@@ -95,6 +95,6 @@ Barycentric_coordinates_2
 Surface_mesh
 Surface_mesh_shortest_path
 Polygon_mesh_processing
-
+Data_classification
 
 
diff --git a/Documentation/doc/Documentation/packages.txt b/Documentation/doc/Documentation/packages.txt
index 2826a9c2959..61416a98177 100644
--- a/Documentation/doc/Documentation/packages.txt
+++ b/Documentation/doc/Documentation/packages.txt
@@ -118,6 +118,7 @@ h1 {
 \package_listing{Point_set_processing_3}
 \package_listing{Point_set_shape_detection_3}
 \package_listing{Stream_lines_2}
+\package_listing{Data_classification}
 
 
 \section PartSearchStructures Spatial Searching and Sorting
diff --git a/Installation/changes.html b/Installation/changes.html
index 66aca1ec06b..debfb2a3d10 100644
--- a/Installation/changes.html
+++ b/Installation/changes.html
@@ -399,6 +399,13 @@ and <code>src/</code> directories).
       <code>CGAL::write_ply_points()</code>
       and <code>CGAL::write_ply_points_and_normals()</code>.</li>
   </ul>
+  <h3>Point Set Shape Detection</h3>
+  <ul>
+    <li>New post-processing
+      algorithm: <code>CGAL::regularize_planes()</code>. This allows the user
+      to favor parallelism, orthogonality, coplanarity and/or axial
+      symmetry between detected planes.</li>
+  </ul>
   <h3>Surface Mesh Parameterization</h3>
   <ul>
     <li><code>LSCM_parameterizer_3</code> now uses by default Eigen
diff --git a/Point_set_processing_3/include/CGAL/IO/read_ply_points.h b/Point_set_processing_3/include/CGAL/IO/read_ply_points.h
index 9843d98375c..3381ccef76f 100644
--- a/Point_set_processing_3/include/CGAL/IO/read_ply_points.h
+++ b/Point_set_processing_3/include/CGAL/IO/read_ply_points.h
@@ -24,6 +24,7 @@
 #include <CGAL/value_type_traits.h>
 #include <CGAL/point_set_processing_assertions.h>
 #include <CGAL/Kernel_traits.h>
+#include <CGAL/IO/io.h>
 
 #include <boost/version.hpp>
 #include <boost/cstdint.hpp>
@@ -82,7 +83,17 @@ namespace internal {
       stream >> s;
       c = static_cast<unsigned char>(s);
     }
+    
+    void read_ascii (std::istream& stream, float& t) const
+    {
+      stream >> iformat(t);
+    }
 
+    void read_ascii (std::istream& stream, double& t) const
+    {
+      stream >> iformat(t);
+    }
+    
     // Default template when Type is not a char type
     template <typename Type>
     void read_ascii (std::istream& stream, Type& t) const
@@ -90,6 +101,7 @@ namespace internal {
       stream >> t;
     }
     
+    
     template <typename Type>
     Type read (std::istream& stream) const
     {
diff --git a/Polyhedron/demo/Polyhedron/CMakeLists.txt b/Polyhedron/demo/Polyhedron/CMakeLists.txt
index e81976cc06b..d7028066472 100644
--- a/Polyhedron/demo/Polyhedron/CMakeLists.txt
+++ b/Polyhedron/demo/Polyhedron/CMakeLists.txt
@@ -242,6 +242,9 @@ if(CGAL_Qt5_FOUND AND Qt5_FOUND AND OPENGL_FOUND AND QGLVIEWER_FOUND)
   add_item(scene_polyhedron_selection_item Scene_polyhedron_selection_item.cpp)
   target_link_libraries(scene_polyhedron_selection_item scene_polyhedron_item_decorator scene_polyhedron_item_k_ring_selection)
 
+  add_item(scene_point_set_classification_item Scene_point_set_classification_item.cpp)
+  target_link_libraries(scene_point_set_classification_item scene_points_with_normal_item scene_color_ramp)
+
   add_item(scene_polyhedron_shortest_path_item Plugins/Surface_mesh/Scene_polyhedron_shortest_path_item.cpp)
 
   add_item(scene_surface_mesh_item Scene_surface_mesh_item.cpp)
@@ -270,9 +273,6 @@ if(CGAL_Qt5_FOUND AND Qt5_FOUND AND OPENGL_FOUND AND QGLVIEWER_FOUND)
 
   target_link_libraries( demo_framework gl_splat)
 
-  
-
-
   foreach( lib 
       demo_framework
       scene_basic_objects
diff --git a/Polyhedron/demo/Polyhedron/Plugins/Point_set/CMakeLists.txt b/Polyhedron/demo/Polyhedron/Plugins/Point_set/CMakeLists.txt
index cd1cd97d797..2a66a138187 100644
--- a/Polyhedron/demo/Polyhedron/Plugins/Point_set/CMakeLists.txt
+++ b/Polyhedron/demo/Polyhedron/Plugins/Point_set/CMakeLists.txt
@@ -39,6 +39,10 @@ endif()
   polyhedron_demo_plugin(point_set_selection_plugin Point_set_selection_plugin ${point_set_selectionUI_FILES})
   target_link_libraries(point_set_selection_plugin scene_points_with_normal_item scene_polylines_item)
 
+  qt5_wrap_ui( point_set_classificationUI_FILES Point_set_classification_widget.ui)
+  polyhedron_demo_plugin(point_set_classification_plugin Point_set_classification_plugin ${point_set_classificationUI_FILES})
+  target_link_libraries(point_set_classification_plugin scene_point_set_classification_item scene_points_with_normal_item scene_polylines_item scene_polygon_soup_item)
+
   qt5_wrap_ui(point_set_shape_detectionUI_FILES Point_set_shape_detection_plugin.ui)
   polyhedron_demo_plugin(point_set_shape_detection_plugin Point_set_shape_detection_plugin ${point_set_shape_detectionUI_FILES})
   target_link_libraries(point_set_shape_detection_plugin scene_polyhedron_item scene_points_with_normal_item scene_polygon_soup_item)
diff --git a/Polyhedron/demo/Polyhedron/Plugins/Point_set/Point_set_classification_plugin.cpp b/Polyhedron/demo/Polyhedron/Plugins/Point_set/Point_set_classification_plugin.cpp
new file mode 100644
index 00000000000..cd1739d9a46
--- /dev/null
+++ b/Polyhedron/demo/Polyhedron/Plugins/Point_set/Point_set_classification_plugin.cpp
@@ -0,0 +1,733 @@
+#include <QtCore/qglobal.h>
+#include <QFileDialog>
+#include <QColorDialog> 
+#include <fstream>
+#include "opengl_tools.h"
+
+#include "Messages_interface.h"
+#include "Scene_points_with_normal_item.h"
+#include "Scene_point_set_classification_item.h"
+#include "Scene_polylines_item.h"
+#include "Scene_polygon_soup_item.h"
+
+#include <CGAL/Three/Scene_interface.h>
+#include <CGAL/Three/Polyhedron_demo_plugin_helper.h>
+
+#include <CGAL/Random.h>
+
+#include "ui_Point_set_classification_widget.h"
+
+#include <QAction>
+#include <QMainWindow>
+#include <QApplication>
+#include <QCheckBox>
+
+#include <map>
+
+#include <boost/graph/adjacency_list.hpp>
+#include <CGAL/boost/graph/split_graph_into_polylines.h>
+
+using namespace CGAL::Three;
+
+class Polyhedron_demo_point_set_classification_plugin :
+  public QObject,
+  public Polyhedron_demo_plugin_helper
+{
+  Q_OBJECT
+    Q_INTERFACES(CGAL::Three::Polyhedron_demo_plugin_interface)
+    Q_PLUGIN_METADATA(IID "com.geometryfactory.PolyhedronDemo.PluginInterface/1.0")
+    
+public:
+  bool applicable(QAction*) const { 
+      return true;
+  }
+  void print_message(QString message) { messages->information(message); }
+  QList<QAction*> actions() const { return QList<QAction*>() << actionPointSetClassification; }
+  
+  using Polyhedron_demo_plugin_helper::init;
+  void init(QMainWindow* mainWindow, CGAL::Three::Scene_interface* scene_interface, Messages_interface* m) {
+    mw = mainWindow;
+    scene = scene_interface;
+    messages = m;
+    actionPointSetClassification = new QAction(tr("Point Set Classification"), mw);
+    connect(actionPointSetClassification, SIGNAL(triggered()), this, SLOT(point_set_classification_action()));
+
+    dock_widget = new QDockWidget("Point Set Classification", mw);
+    dock_widget->setVisible(false);
+
+    ui_widget.setupUi(dock_widget);
+    addDockWidget(dock_widget);
+
+    add_class_rows();
+
+    color_att = QColor (75, 75, 77);
+    
+    connect(ui_widget.create_from_file,  SIGNAL(clicked()), this,
+            SLOT(on_create_from_file_button_clicked()));
+    connect(ui_widget.create_from_item,  SIGNAL(clicked()), this,
+            SLOT(on_create_from_item_button_clicked()));
+    connect(ui_widget.estimate_parameters,  SIGNAL(clicked()), this,
+            SLOT(on_estimate_parameters_button_clicked()));
+    connect(ui_widget.compute_features,  SIGNAL(clicked()), this,
+            SLOT(on_compute_features_button_clicked()));
+    connect(ui_widget.compute_ransac,  SIGNAL(clicked()), this,
+            SLOT(on_compute_ransac_button_clicked()));
+    connect(ui_widget.random_weights,  SIGNAL(clicked()), this,
+            SLOT(on_random_weights_button_clicked()));
+    connect(ui_widget.display,  SIGNAL(currentIndexChanged(int)), this,
+            SLOT(on_display_button_clicked(int)));
+    connect(ui_widget.run,  SIGNAL(clicked()), this,
+            SLOT(on_run_button_clicked()));
+    connect(ui_widget.scatterSlider,  SIGNAL(sliderReleased()), this,
+            SLOT(on_run_button_clicked()));
+    connect(ui_widget.planaritySlider,  SIGNAL(sliderReleased()), this,
+            SLOT(on_run_button_clicked()));
+    connect(ui_widget.horizontalitySlider,  SIGNAL(sliderReleased()), this,
+            SLOT(on_run_button_clicked()));
+    connect(ui_widget.elevationSlider,  SIGNAL(sliderReleased()), this,
+            SLOT(on_run_button_clicked()));
+    connect(ui_widget.colorSlider,  SIGNAL(sliderReleased()), this,
+            SLOT(on_run_button_clicked()));
+    connect(ui_widget.save_config,  SIGNAL(clicked()), this,
+            SLOT(on_save_config_button_clicked()));
+    connect(ui_widget.load_config,  SIGNAL(clicked()), this,
+            SLOT(on_load_config_button_clicked()));
+    connect(ui_widget.colorButton,  SIGNAL(clicked()), this,
+            SLOT(on_colorButton_clicked()));
+    connect(ui_widget.run_with_smoothing,  SIGNAL(clicked()), this,
+            SLOT(on_run_with_smoothing_button_clicked()));
+    connect(ui_widget.run_with_ransac,  SIGNAL(clicked()), this,
+            SLOT(on_run_with_ransac_button_clicked()));
+    connect(ui_widget.compute_clusters,  SIGNAL(clicked()), this,
+            SLOT(on_compute_clusters_button_clicked()));
+    connect(ui_widget.save,  SIGNAL(clicked()), this,
+            SLOT(on_save_button_clicked()));
+    connect(ui_widget.generate_point_set_items,  SIGNAL(clicked()), this,
+            SLOT(on_generate_point_set_items_button_clicked()));
+    connect(ui_widget.extract_facades,  SIGNAL(clicked()), this,
+            SLOT(on_extract_facades_button_clicked()));
+    connect(ui_widget.extract_2d_outline,  SIGNAL(clicked()), this,
+            SLOT(on_extract_2d_outline_button_clicked()));
+
+  }
+  virtual void closure()
+  {
+    dock_widget->hide();
+  }
+
+  void add_class_rows()
+  {
+    class_rows.push_back (ClassRow (dock_widget, "Ground", true, 2, 2, 2, 2, 1));
+    class_rows.push_back (ClassRow (dock_widget, "Vegetation", true, 0, 0, 1, 0, 1));
+    class_rows.push_back (ClassRow (dock_widget, "Road", false, 2, 2, 2, 2, 0));
+    class_rows.push_back (ClassRow (dock_widget, "Roof", true, 2, 1, 2, 0, 1));
+    class_rows.push_back (ClassRow (dock_widget, "Facade", true, 1, 2, 0, 0, 1));
+    class_rows.push_back (ClassRow (dock_widget, "Building", false, 2, 1, 0, 1, 1));
+
+    for (std::size_t i = 0; i < class_rows.size(); ++ i)
+      {
+        ui_widget.gridLayout->addWidget (class_rows[i].label, 2 + (int)i, 0);
+        ui_widget.gridLayout->addWidget (class_rows[i].checkbox, 2 + (int)i, 1);
+        for (std::size_t j = 0; j < class_rows[i].combo.size(); ++ j)
+          ui_widget.gridLayout->addWidget (class_rows[i].combo[j], 2 + (int)i, 2 + (int)j);
+      }
+  }
+
+public Q_SLOTS:
+  
+  void point_set_classification_action()
+  { 
+    dock_widget->show();
+    dock_widget->raise();
+  }
+  
+  void on_create_from_file_button_clicked()
+  {
+    CGAL::Random rand(time(0));
+    
+    QString filename = QFileDialog::getOpenFileName(mw,
+                                                    tr("Open PLY point set"),
+                                                    ".",
+                                                    "PLY point set (*.ply);;All Files (*)");
+
+    if (filename == QString())
+      return;
+    
+    QFileInfo fi(filename);
+    QString name = fi.fileName();
+    name.chop(4);
+    std::ifstream in(filename.toUtf8(), std::ios_base::binary);
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    Scene_point_set_classification_item* new_item
+      = new Scene_point_set_classification_item ();
+    new_item->read_ply_point_set (in, ui_widget.gridResolutionDoubleSpinBox->value());
+
+    std::vector<Scene_point_set_classification_item*> items;
+    if (new_item->segment_point_set (ui_widget.max_nb_points->value(), std::back_inserter (items)))
+      {
+        for (std::size_t i = 0; i < items.size(); ++ i)
+          {
+            unsigned char r, g, b;
+
+            r = static_cast<unsigned char>(64 + rand.get_int(0, 192));
+            g = static_cast<unsigned char>(64 + rand.get_int(0, 192));
+            b = static_cast<unsigned char>(64 + rand.get_int(0, 192));
+            items[i]->setRbgColor (r, g, b);
+            items[i]->setName(QString("%1 %2 (classification)").arg(name).arg (i+1));
+            int item_id = scene->addItem(items[i]);
+            scene->setSelectedItem(item_id);
+          }
+        delete new_item;
+      }
+    else
+      {
+        new_item->setName(QString("%1 (classification)").arg(name));
+        int item_id = scene->addItem(new_item);
+        scene->setSelectedItem(item_id);
+      }
+    
+    QApplication::restoreOverrideCursor();
+  }
+
+  void on_create_from_item_button_clicked()
+  {
+    Scene_points_with_normal_item* points_item = getSelectedItem<Scene_points_with_normal_item>();
+    if(!points_item)
+      {
+        print_message("Error: there is no selected point set item!");
+        return; 
+      }
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    Scene_point_set_classification_item* new_item
+      = new Scene_point_set_classification_item (points_item, ui_widget.gridResolutionDoubleSpinBox->value());
+    int item_id = scene->addItem(new_item);
+    new_item->setName(QString("%1 (classification)").arg(points_item->name()));
+    scene->setSelectedItem(item_id);
+    QApplication::restoreOverrideCursor();
+  }
+
+  void on_estimate_parameters_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    QTime time;
+    time.start();
+
+    double grid_resolution = 0., radius_neighbors = 0., radius_dtm = 0.;
+    classification_item->estimate_parameters (grid_resolution, radius_neighbors, radius_dtm);
+    std::cerr << "Parameters computed in " << time.elapsed() / 1000 << " second(s)" << std::endl;
+    ui_widget.gridResolutionDoubleSpinBox->setValue(grid_resolution);
+    ui_widget.radiusNeighborsDoubleSpinBox->setValue(radius_neighbors);
+    ui_widget.radiusDTMDoubleSpinBox->setValue(radius_dtm);
+    QApplication::restoreOverrideCursor();
+  }
+
+  void run (Scene_point_set_classification_item* classification_item, int method)
+  {
+    classification_item->run (ui_widget.scatterSlider->value() /
+                              (double)(ui_widget.scatterSlider->maximum()+1),
+                              ui_widget.planaritySlider->value() /
+                              (double)(ui_widget.planaritySlider->maximum()+1),
+                              ui_widget.horizontalitySlider->value() /
+                              (double)(ui_widget.horizontalitySlider->maximum()+1),
+                              ui_widget.elevationSlider->value() /
+                              (double)(ui_widget.elevationSlider->maximum()+1),
+                              ui_widget.colorSlider->value() /
+                              (double)(ui_widget.colorSlider->maximum()+1),
+                              ui_widget.multiply_weights->isChecked (),
+                              class_rows,
+                              method,
+                              ui_widget.smoothingDoubleSpinBox->value());
+  }
+
+  void on_compute_features_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    QTime time;
+    time.start();
+    classification_item->compute_features (ui_widget.gridResolutionDoubleSpinBox->value(),
+                                           ui_widget.radiusNeighborsDoubleSpinBox->value(),
+                                           ui_widget.radiusDTMDoubleSpinBox->value(),
+                                           color_att);
+
+    run (classification_item, 0);
+    std::cerr << "Features computed in " << time.elapsed() / 1000 << " second(s)" << std::endl;
+    QApplication::restoreOverrideCursor();
+    scene->itemChanged(classification_item);
+  }
+
+  void on_compute_ransac_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    QTime time;
+    time.start();
+    classification_item->compute_ransac (ui_widget.gridResolutionDoubleSpinBox->value());
+    std::cerr << "RANSAC computed in " << time.elapsed() / 1000 << " second(s)" << std::endl;
+    QApplication::restoreOverrideCursor();
+    scene->itemChanged(classification_item);
+  }
+
+  void on_save_config_button_clicked()
+  {
+    QString filename = QFileDialog::getSaveFileName(mw,
+                                                    tr("Save classification configuration"),
+                                                    QString("default.conf"),
+                                                    "Config file (*.conf);;");
+    if (filename == QString())
+      return;
+
+    std::ofstream out(filename.toUtf8());
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+
+    out << ui_widget.gridResolutionDoubleSpinBox->value() << " "
+        << ui_widget.radiusNeighborsDoubleSpinBox->value() << " "
+        << ui_widget.radiusDTMDoubleSpinBox->value() << " " << std::endl
+        << ui_widget.scatterSlider->value() << " "
+        << ui_widget.planaritySlider->value() << " "
+        << ui_widget.horizontalitySlider->value() << " "
+        << ui_widget.elevationSlider->value() << " "
+        << ui_widget.colorSlider->value() << std::endl;
+
+    for (std::size_t i = 0; i < class_rows.size(); ++ i)
+      {
+        out << (class_rows[i].checkbox->isChecked() ? "1" : "0") << " ";
+        for (std::size_t j = 0; j < class_rows[j].combo.size(); ++ j)
+          out << class_rows[i].combo[j]->currentIndex() << " ";
+        out << std::endl;
+      }
+    
+    QApplication::restoreOverrideCursor();
+
+    out.close();
+  }
+
+  void on_load_config_button_clicked()
+  {
+    QString filename = QFileDialog::getOpenFileName(mw,
+                                                    tr("Open classification configuration"),
+                                                    ".",
+                                                    "Config file (*.conf);;All Files (*)");
+
+    if (filename == QString())
+      return;
+
+    std::ifstream in(filename.toUtf8());
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+
+    double gridResolution = 0., radiusNeighbors = 0., radiusDTM = 0.;
+    if (!(in >> gridResolution >> radiusNeighbors >> radiusDTM))
+      {
+        std::cerr << "Error: can't read config file." << std::endl;
+        return;
+      }
+
+    int scatter = 0, planarity = 0, horizontality = 0, elevation = 0, color = 0;
+    if (!(in >> scatter >> planarity >> horizontality >> elevation >> color))
+      {
+        std::cerr << "Error: can't read config file." << std::endl;
+        return;
+      }
+    ui_widget.gridResolutionDoubleSpinBox->setValue(gridResolution);
+    ui_widget.radiusNeighborsDoubleSpinBox->setValue(radiusNeighbors);
+    ui_widget.radiusDTMDoubleSpinBox->setValue(radiusDTM);
+    ui_widget.scatterSlider->setValue (scatter);
+    ui_widget.planaritySlider->setValue (planarity);
+    ui_widget.horizontalitySlider->setValue (horizontality);
+    ui_widget.elevationSlider->setValue (elevation);
+    ui_widget.colorSlider->setValue (color);
+
+    for (std::size_t i = 0; i < class_rows.size(); ++ i)
+      {
+        int checked = 0;
+        if (!(in >> checked))
+          {
+            std::cerr << "Error: can't read config file." << std::endl;
+            return;
+          }
+        class_rows[i].checkbox->setChecked (checked);
+
+        for (std::size_t j = 0; j < class_rows[j].combo.size(); ++ j)
+          {
+            int index = 0;
+            if (!(in >> index))
+              {
+                std::cerr << "Error: can't read config file." << std::endl;
+                return;
+              }
+            class_rows[i].combo[j]->setCurrentIndex (index);
+          }
+      }
+    
+    QApplication::restoreOverrideCursor();
+
+    in.close();
+  }
+
+
+  void on_colorButton_clicked()
+  {
+    color_att = QColorDialog::getColor (QColor (33, 37, 33), (QWidget*)mw,
+                                        QString ("Select favored color"));
+  }
+
+
+  void on_random_weights_button_clicked()
+  {
+    ui_widget.scatterSlider->setValue (rand() % ui_widget.scatterSlider->maximum());
+    ui_widget.planaritySlider->setValue (rand() % ui_widget.planaritySlider->maximum());
+    ui_widget.horizontalitySlider->setValue (rand() % ui_widget.horizontalitySlider->maximum());
+    ui_widget.elevationSlider->setValue (rand() % ui_widget.elevationSlider->maximum());
+    ui_widget.colorSlider->setValue (rand() % ui_widget.colorSlider->maximum());
+
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    run (classification_item, 0);
+    QApplication::restoreOverrideCursor();
+    scene->itemChanged(classification_item);
+  }
+
+  void on_display_button_clicked(int index)
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      return; 
+
+    classification_item->change_color (index);
+    scene->itemChanged(classification_item);
+  }
+
+  void on_run_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    run (classification_item, 0);
+    QApplication::restoreOverrideCursor();
+    scene->itemChanged(classification_item);
+  }
+
+  void on_run_with_smoothing_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    QTime time;
+    time.start();
+    run (classification_item, 1);
+    std::cerr << "Smoothed classification computed in " << time.elapsed() / 1000 << " second(s)" << std::endl;
+    QApplication::restoreOverrideCursor();
+    scene->itemChanged(classification_item);
+  }
+
+  void on_run_with_ransac_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    QTime time;
+    time.start();
+    run (classification_item, 2);
+    std::cerr << "RANSAC-guided classification computed in " << time.elapsed() / 1000 << " second(s)" << std::endl;
+    QApplication::restoreOverrideCursor();
+    scene->itemChanged(classification_item);
+  }
+
+  void on_compute_clusters_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    QTime time;
+    time.start();
+    classification_item->compute_clusters (ui_widget.radiusNeighborsDoubleSpinBox->value());
+    std::cerr << "Clusters computed in " << time.elapsed() / 1000 << " second(s)" << std::endl;
+    QApplication::restoreOverrideCursor();
+    scene->itemChanged(classification_item);
+  }
+ 
+ void on_save_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+
+    QString filename = QFileDialog::getSaveFileName(mw,
+                                                    tr("Save PLY classified point set"),
+                                                    QString("%1.ply").arg(classification_item->name()),
+                                                    "PLY point set (*.ply);;");
+
+    if (filename == QString())
+      return;
+
+    std::ofstream out(filename.toUtf8());
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+    classification_item->write_ply_point_set (out);
+    QApplication::restoreOverrideCursor();
+
+    out.close();
+  }
+
+  void on_generate_point_set_items_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+
+    std::vector<Scene_points_with_normal_item*> new_items;
+    for (std::size_t i = 0; i < 6; ++ i)
+      new_items.push_back (new Scene_points_with_normal_item);
+
+    classification_item->generate_point_sets (new_items);
+
+    for (std::size_t i = 0; i < new_items.size(); ++ i)
+      {
+        if (new_items[i]->point_set()->empty())
+          delete new_items[i];
+        else
+          {
+            if (i == 0) // Vegetation
+              {
+                new_items[i]->setName(QString("%1 (vegetation)")
+                                      .arg(classification_item->name()));
+                new_items[i]->setColor(QColor(0, 255, 27));
+              }
+            else if (i == 1) // Ground
+              {
+                new_items[i]->setName(QString("%1 (ground)")
+                                      .arg(classification_item->name()));
+                new_items[i]->setColor(QColor(245, 180, 0));
+              }
+            else if (i == 2) // Road
+              {
+                new_items[i]->setName(QString("%1 (road)")
+                                      .arg(classification_item->name()));
+                new_items[i]->setColor(QColor(200, 200, 200));
+              }
+            else if (i == 3) // Roof
+              {
+                new_items[i]->setName(QString("%1 (roofs)")
+                                      .arg(classification_item->name()));
+                new_items[i]->setColor(QColor(255, 0, 170));
+              }
+            else if (i == 4) // Facade
+              {
+                new_items[i]->setName(QString("%1 (facades)")
+                                      .arg(classification_item->name()));
+                new_items[i]->setColor(QColor(100, 0, 255));
+              }
+            else if (i == 5) // Building
+              {
+                new_items[i]->setName(QString("%1 (buildings)")
+                                      .arg(classification_item->name()));
+                new_items[i]->setColor(QColor(0, 114, 225));
+              }
+            scene->addItem (new_items[i]);
+          }
+      }
+    
+    QApplication::restoreOverrideCursor();
+  }
+
+  void on_extract_2d_outline_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+
+    std::vector<Kernel::Triangle_3> faces;
+    classification_item->extract_building_map (ui_widget.radiusNeighborsDoubleSpinBox->value(),
+                                               faces);
+
+    Scene_polygon_soup_item* new_item
+      = new Scene_polygon_soup_item ();
+    new_item->setColor(Qt::magenta);
+    new_item->setRenderingMode(FlatPlusEdges);
+    new_item->init_polygon_soup(faces.size() * 3, faces.size());
+
+    for (std::size_t i = 0; i < faces.size(); ++ i)
+      {
+        for (std::size_t j = 0; j < 3; ++ j)
+          new_item->new_vertex (faces[i][j].x(), faces[i][j].y(), faces[i][j].z());
+        new_item->new_triangle (3 * i, 3 * i + 1, 3 * i + 2);
+      }
+    scene->addItem (new_item);
+// std::vector<Kernel::Point_3> outline;
+//     classification_item->extract_2d_outline(ui_widget.radiusNeighborsDoubleSpinBox->value(),
+//                                             outline);
+
+    // Scene_polylines_item* item = new Scene_polylines_item;
+
+    // for (std::size_t i = 0; i < outline.size(); i += 2)
+    //   {
+    //     item->polylines.push_back (std::vector<Kernel::Point_3>());
+    //     item->polylines.back().push_back (outline[i]);
+    //     item->polylines.back().push_back (outline[i+1]);
+    //   }
+    // item->setName(QString("%1 (2D outline)")
+    //               .arg(classification_item->name()));
+    // item->setColor(Qt::black);
+    // item->invalidateOpenGLBuffers();
+    // scene->addItem (item);
+
+    QApplication::restoreOverrideCursor();
+  }
+
+  void on_extract_facades_button_clicked()
+  {
+    Scene_point_set_classification_item* classification_item
+      = getSelectedItem<Scene_point_set_classification_item>();
+    if(!classification_item)
+      {
+        print_message("Error: there is no point set classification item!");
+        return; 
+      }
+    
+    QApplication::setOverrideCursor(Qt::WaitCursor);
+
+    std::vector<Kernel::Triangle_3> faces;
+    classification_item->extract_facades (ui_widget.radiusNeighborsDoubleSpinBox->value(),
+                                          faces);
+
+    Scene_polygon_soup_item* new_item
+      = new Scene_polygon_soup_item ();
+    new_item->setColor(Qt::cyan);
+    new_item->setRenderingMode(FlatPlusEdges);
+    new_item->init_polygon_soup(faces.size() * 3, faces.size());
+
+    for (std::size_t i = 0; i < faces.size(); ++ i)
+      {
+        for (std::size_t j = 0; j < 3; ++ j)
+          new_item->new_vertex (faces[i][j].x(), faces[i][j].y(), faces[i][j].z());
+        new_item->new_triangle (3 * i, 3 * i + 1, 3 * i + 2);
+      }
+    scene->addItem (new_item);
+
+    QApplication::restoreOverrideCursor();
+  }
+
+private:
+  Messages_interface* messages;
+  QAction* actionPointSetClassification;
+
+  QDockWidget* dock_widget;
+
+  struct ClassRow
+  {
+    QLabel* label;
+    QCheckBox* checkbox;
+    std::vector<QComboBox*> combo;
+
+    ClassRow (QWidget* parent, const char* name, bool checked,
+              int scat, int plan, int hori, int elev, int colo)
+    {
+      label = new QLabel (name, parent);
+      checkbox = new QCheckBox (parent);
+      checkbox->setChecked(checked);
+      for (std::size_t i = 0; i < 5; ++ i)
+        {
+          combo.push_back (new QComboBox (parent));
+          combo.back()->addItem ("Favored");
+          combo.back()->addItem ("Neutral");
+          combo.back()->addItem ("Penalized");
+          if (i == 0)
+            combo.back()->setCurrentIndex (scat);
+          else if (i == 1)
+            combo.back()->setCurrentIndex (plan);
+          else if (i == 2)
+            combo.back()->setCurrentIndex (hori);
+          else if (i == 3)
+            combo.back()->setCurrentIndex (elev);
+          else if (i == 4)
+            combo.back()->setCurrentIndex (colo);
+        }
+    }
+    ~ClassRow ()
+    {
+    }
+
+  };
+
+  std::vector<ClassRow> class_rows;
+  
+  Ui::PointSetClassification ui_widget;
+
+  QColor color_att;
+
+}; // end Polyhedron_demo_point_set_classification_plugin
+
+#include "Point_set_classification_plugin.moc"
diff --git a/Polyhedron/demo/Polyhedron/Plugins/Point_set/Point_set_classification_widget.ui b/Polyhedron/demo/Polyhedron/Plugins/Point_set/Point_set_classification_widget.ui
new file mode 100644
index 00000000000..06a7e57db7c
--- /dev/null
+++ b/Polyhedron/demo/Polyhedron/Plugins/Point_set/Point_set_classification_widget.ui
@@ -0,0 +1,781 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<ui version="4.0">
+ <class>PointSetClassification</class>
+ <widget class="QDockWidget" name="PointSetClassification">
+  <property name="geometry">
+   <rect>
+    <x>0</x>
+    <y>0</y>
+    <width>511</width>
+    <height>572</height>
+   </rect>
+  </property>
+  <property name="windowTitle">
+   <string>Point set classification</string>
+  </property>
+  <widget class="QWidget" name="dockWidgetContents">
+   <layout class="QVBoxLayout" name="verticalLayout_2">
+    <item>
+     <widget class="QTabWidget" name="tabWidget">
+      <property name="currentIndex">
+       <number>2</number>
+      </property>
+      <widget class="QWidget" name="tab">
+       <attribute name="title">
+        <string>I/O</string>
+       </attribute>
+       <layout class="QVBoxLayout" name="verticalLayout">
+        <item>
+         <spacer name="verticalSpacer_7">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+        <item>
+         <widget class="QLabel" name="label_3">
+          <property name="text">
+           <string>Input</string>
+          </property>
+         </widget>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout_9">
+          <item>
+           <spacer name="horizontalSpacer">
+            <property name="orientation">
+             <enum>Qt::Horizontal</enum>
+            </property>
+            <property name="sizeHint" stdset="0">
+             <size>
+              <width>40</width>
+              <height>20</height>
+             </size>
+            </property>
+           </spacer>
+          </item>
+          <item>
+           <widget class="QLabel" name="label_6">
+            <property name="text">
+             <string>Segment point sets larger than</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QSpinBox" name="max_nb_points">
+            <property name="sizePolicy">
+             <sizepolicy hsizetype="Maximum" vsizetype="Fixed">
+              <horstretch>0</horstretch>
+              <verstretch>0</verstretch>
+             </sizepolicy>
+            </property>
+            <property name="suffix">
+             <string/>
+            </property>
+            <property name="minimum">
+             <number>100</number>
+            </property>
+            <property name="maximum">
+             <number>100000000</number>
+            </property>
+            <property name="singleStep">
+             <number>100000</number>
+            </property>
+            <property name="value">
+             <number>5000000</number>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QLabel" name="label_7">
+            <property name="text">
+             <string>points</string>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout">
+          <item>
+           <widget class="QPushButton" name="create_from_file">
+            <property name="text">
+             <string>Create items from PLY</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QPushButton" name="create_from_item">
+            <property name="text">
+             <string>Create item from point set item</string>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <spacer name="verticalSpacer">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+        <item>
+         <widget class="QLabel" name="label_5">
+          <property name="text">
+           <string>Classification parameters</string>
+          </property>
+         </widget>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout_8">
+          <item>
+           <widget class="QPushButton" name="load_config">
+            <property name="text">
+             <string>Load config</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QPushButton" name="save_config">
+            <property name="text">
+             <string>Save config</string>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <spacer name="verticalSpacer_4">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+        <item>
+         <widget class="QLabel" name="label_4">
+          <property name="text">
+           <string>Output</string>
+          </property>
+         </widget>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout_5">
+          <item>
+           <widget class="QPushButton" name="save">
+            <property name="font">
+             <font>
+              <weight>50</weight>
+              <bold>false</bold>
+             </font>
+            </property>
+            <property name="text">
+             <string>Save colored point set</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QPushButton" name="generate_point_set_items">
+            <property name="text">
+             <string>Generate point set items</string>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout_7">
+          <item>
+           <widget class="QPushButton" name="extract_facades">
+            <property name="text">
+             <string>Extract facades</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QPushButton" name="extract_2d_outline">
+            <property name="text">
+             <string>Extract 2D outline</string>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <spacer name="verticalSpacer_8">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+       </layout>
+      </widget>
+      <widget class="QWidget" name="tab_2">
+       <attribute name="title">
+        <string>Features</string>
+       </attribute>
+       <layout class="QVBoxLayout" name="verticalLayout_5">
+        <item>
+         <spacer name="verticalSpacer_6">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+        <item>
+         <widget class="QPushButton" name="estimate_parameters">
+          <property name="text">
+           <string>Estimate parameters</string>
+          </property>
+         </widget>
+        </item>
+        <item>
+         <layout class="QFormLayout" name="formLayout">
+          <property name="fieldGrowthPolicy">
+           <enum>QFormLayout::AllNonFixedFieldsGrow</enum>
+          </property>
+          <item row="0" column="0">
+           <widget class="QLabel" name="gridResolutionLabel">
+            <property name="text">
+             <string>Grid resolution</string>
+            </property>
+           </widget>
+          </item>
+          <item row="0" column="1">
+           <widget class="QDoubleSpinBox" name="gridResolutionDoubleSpinBox">
+            <property name="decimals">
+             <number>3</number>
+            </property>
+            <property name="minimum">
+             <double>0.001000000000000</double>
+            </property>
+            <property name="singleStep">
+             <double>0.010000000000000</double>
+            </property>
+            <property name="value">
+             <double>0.100000000000000</double>
+            </property>
+           </widget>
+          </item>
+          <item row="1" column="0">
+           <widget class="QLabel" name="radiusNeighborsLabel">
+            <property name="text">
+             <string>Radius neighbors</string>
+            </property>
+           </widget>
+          </item>
+          <item row="1" column="1">
+           <widget class="QDoubleSpinBox" name="radiusNeighborsDoubleSpinBox">
+            <property name="decimals">
+             <number>3</number>
+            </property>
+            <property name="minimum">
+             <double>0.001000000000000</double>
+            </property>
+            <property name="singleStep">
+             <double>0.010000000000000</double>
+            </property>
+            <property name="value">
+             <double>0.500000000000000</double>
+            </property>
+           </widget>
+          </item>
+          <item row="2" column="0">
+           <widget class="QLabel" name="radiusDTMLabel">
+            <property name="text">
+             <string>Radius DTM</string>
+            </property>
+           </widget>
+          </item>
+          <item row="2" column="1">
+           <widget class="QDoubleSpinBox" name="radiusDTMDoubleSpinBox">
+            <property name="decimals">
+             <number>3</number>
+            </property>
+            <property name="minimum">
+             <double>0.001000000000000</double>
+            </property>
+            <property name="singleStep">
+             <double>0.010000000000000</double>
+            </property>
+            <property name="value">
+             <double>2.500000000000000</double>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <spacer name="verticalSpacer_3">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout_3">
+          <item>
+           <widget class="QPushButton" name="compute_features">
+            <property name="text">
+             <string>Compute features</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QPushButton" name="compute_ransac">
+            <property name="text">
+             <string>Compute RANSAC</string>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <spacer name="verticalSpacer_5">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+       </layout>
+      </widget>
+      <widget class="QWidget" name="tab_3">
+       <attribute name="title">
+        <string>Classification</string>
+       </attribute>
+       <layout class="QVBoxLayout" name="verticalLayout_7">
+        <item>
+         <spacer name="verticalSpacer_9">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout_4">
+          <item>
+           <widget class="QPushButton" name="multiply_weights">
+            <property name="text">
+             <string>Multiply weights</string>
+            </property>
+            <property name="checkable">
+             <bool>true</bool>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QPushButton" name="random_weights">
+            <property name="text">
+             <string>Try random weights</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QComboBox" name="display">
+            <property name="currentIndex">
+             <number>0</number>
+            </property>
+            <item>
+             <property name="text">
+              <string>Real colors</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>Classification</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>Confidence</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>Scatter</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>Planarity</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>Horizontality</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>Elevation</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>Color</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>RANSAC</string>
+             </property>
+            </item>
+            <item>
+             <property name="text">
+              <string>Clusters</string>
+             </property>
+            </item>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <layout class="QGridLayout" name="gridLayout">
+          <item row="1" column="4" alignment="Qt::AlignHCenter">
+           <widget class="QDial" name="horizontalitySlider">
+            <property name="maximumSize">
+             <size>
+              <width>75</width>
+              <height>16777215</height>
+             </size>
+            </property>
+            <property name="maximum">
+             <number>999</number>
+            </property>
+            <property name="sliderPosition">
+             <number>999</number>
+            </property>
+            <property name="notchTarget">
+             <double>50.000000000000000</double>
+            </property>
+            <property name="notchesVisible">
+             <bool>true</bool>
+            </property>
+           </widget>
+          </item>
+          <item row="1" column="5" alignment="Qt::AlignHCenter">
+           <widget class="QDial" name="elevationSlider">
+            <property name="maximumSize">
+             <size>
+              <width>75</width>
+              <height>16777215</height>
+             </size>
+            </property>
+            <property name="maximum">
+             <number>999</number>
+            </property>
+            <property name="sliderPosition">
+             <number>999</number>
+            </property>
+            <property name="notchTarget">
+             <double>50.000000000000000</double>
+            </property>
+            <property name="notchesVisible">
+             <bool>true</bool>
+            </property>
+           </widget>
+          </item>
+          <item row="1" column="3" alignment="Qt::AlignHCenter">
+           <widget class="QDial" name="planaritySlider">
+            <property name="maximumSize">
+             <size>
+              <width>75</width>
+              <height>16777215</height>
+             </size>
+            </property>
+            <property name="maximum">
+             <number>999</number>
+            </property>
+            <property name="sliderPosition">
+             <number>999</number>
+            </property>
+            <property name="wrapping">
+             <bool>false</bool>
+            </property>
+            <property name="notchTarget">
+             <double>50.000000000000000</double>
+            </property>
+            <property name="notchesVisible">
+             <bool>true</bool>
+            </property>
+           </widget>
+          </item>
+          <item row="1" column="2" alignment="Qt::AlignHCenter">
+           <widget class="QDial" name="scatterSlider">
+            <property name="maximumSize">
+             <size>
+              <width>75</width>
+              <height>16777215</height>
+             </size>
+            </property>
+            <property name="maximum">
+             <number>999</number>
+            </property>
+            <property name="sliderPosition">
+             <number>999</number>
+            </property>
+            <property name="notchTarget">
+             <double>50.000000000000000</double>
+            </property>
+            <property name="notchesVisible">
+             <bool>true</bool>
+            </property>
+           </widget>
+          </item>
+          <item row="0" column="4" alignment="Qt::AlignHCenter">
+           <widget class="QLabel" name="horizontalityLabel">
+            <property name="text">
+             <string>Horizontality</string>
+            </property>
+           </widget>
+          </item>
+          <item row="1" column="1" alignment="Qt::AlignHCenter|Qt::AlignBottom">
+           <widget class="QLabel" name="label_2">
+            <property name="font">
+             <font>
+              <weight>75</weight>
+              <bold>true</bold>
+             </font>
+            </property>
+            <property name="text">
+             <string>Active</string>
+            </property>
+           </widget>
+          </item>
+          <item row="0" column="5" alignment="Qt::AlignHCenter">
+           <widget class="QLabel" name="elevationLabel">
+            <property name="text">
+             <string>Elevation</string>
+            </property>
+           </widget>
+          </item>
+          <item row="0" column="6">
+           <widget class="QPushButton" name="colorButton">
+            <property name="text">
+             <string>Color</string>
+            </property>
+           </widget>
+          </item>
+          <item row="1" column="0" alignment="Qt::AlignHCenter|Qt::AlignBottom">
+           <widget class="QLabel" name="label">
+            <property name="font">
+             <font>
+              <weight>75</weight>
+              <bold>true</bold>
+             </font>
+            </property>
+            <property name="text">
+             <string>Class</string>
+            </property>
+           </widget>
+          </item>
+          <item row="0" column="2" alignment="Qt::AlignHCenter">
+           <widget class="QLabel" name="scatterLabel">
+            <property name="text">
+             <string>Scatter</string>
+            </property>
+           </widget>
+          </item>
+          <item row="0" column="3" alignment="Qt::AlignHCenter">
+           <widget class="QLabel" name="planarityLabel">
+            <property name="text">
+             <string>Non-planarity</string>
+            </property>
+           </widget>
+          </item>
+          <item row="1" column="6" alignment="Qt::AlignHCenter">
+           <widget class="QDial" name="colorSlider">
+            <property name="maximumSize">
+             <size>
+              <width>75</width>
+              <height>16777215</height>
+             </size>
+            </property>
+            <property name="maximum">
+             <number>999</number>
+            </property>
+            <property name="value">
+             <number>999</number>
+            </property>
+            <property name="notchTarget">
+             <double>50.000000000000000</double>
+            </property>
+            <property name="notchesVisible">
+             <bool>true</bool>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <spacer name="verticalSpacer_2">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout_2">
+          <item>
+           <widget class="QPushButton" name="run">
+            <property name="font">
+             <font>
+              <weight>75</weight>
+              <bold>true</bold>
+             </font>
+            </property>
+            <property name="text">
+             <string>Run (quick)</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QFrame" name="frame">
+            <property name="frameShape">
+             <enum>QFrame::StyledPanel</enum>
+            </property>
+            <property name="frameShadow">
+             <enum>QFrame::Raised</enum>
+            </property>
+            <layout class="QHBoxLayout" name="horizontalLayout_10">
+             <item>
+              <widget class="QPushButton" name="run_with_smoothing">
+               <property name="font">
+                <font>
+                 <weight>75</weight>
+                 <bold>true</bold>
+                </font>
+               </property>
+               <property name="text">
+                <string>Run with smoothing</string>
+               </property>
+              </widget>
+             </item>
+             <item>
+              <widget class="QDoubleSpinBox" name="smoothingDoubleSpinBox">
+               <property name="sizePolicy">
+                <sizepolicy hsizetype="Minimum" vsizetype="Fixed">
+                 <horstretch>0</horstretch>
+                 <verstretch>0</verstretch>
+                </sizepolicy>
+               </property>
+               <property name="toolTip">
+                <string>Smoothing strenght</string>
+               </property>
+               <property name="maximum">
+                <double>10.000000000000000</double>
+               </property>
+               <property name="singleStep">
+                <double>0.100000000000000</double>
+               </property>
+               <property name="value">
+                <double>0.500000000000000</double>
+               </property>
+              </widget>
+             </item>
+            </layout>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <layout class="QHBoxLayout" name="horizontalLayout_6">
+          <item>
+           <widget class="QPushButton" name="run_with_ransac">
+            <property name="font">
+             <font>
+              <weight>75</weight>
+              <bold>true</bold>
+             </font>
+            </property>
+            <property name="text">
+             <string>Run with RANSAC</string>
+            </property>
+           </widget>
+          </item>
+          <item>
+           <widget class="QPushButton" name="compute_clusters">
+            <property name="text">
+             <string>Compute clusters</string>
+            </property>
+           </widget>
+          </item>
+         </layout>
+        </item>
+        <item>
+         <spacer name="verticalSpacer_10">
+          <property name="orientation">
+           <enum>Qt::Vertical</enum>
+          </property>
+          <property name="sizeHint" stdset="0">
+           <size>
+            <width>20</width>
+            <height>40</height>
+           </size>
+          </property>
+         </spacer>
+        </item>
+       </layout>
+      </widget>
+     </widget>
+    </item>
+   </layout>
+  </widget>
+ </widget>
+ <resources/>
+ <connections/>
+</ui>
diff --git a/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item.cpp b/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item.cpp
new file mode 100644
index 00000000000..4346b018894
--- /dev/null
+++ b/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item.cpp
@@ -0,0 +1,1236 @@
+#include "Scene_point_set_classification_item.h"
+#include "Color_ramp.h"
+
+#include <CGAL/IO/read_ply_points.h>
+#include <CGAL/IO/write_ply_points.h>
+#include <CGAL/Timer.h>
+#include <CGAL/Memory_sizer.h>
+
+#include <CGAL/Three/Viewer_interface.h>
+#include <CGAL/Orthogonal_k_neighbor_search.h>
+#include <CGAL/jet_estimate_normals.h>
+#include <CGAL/Search_traits_3.h>
+#include <CGAL/Shape_detection_3.h>
+#include <CGAL/regularize_planes.h>
+#include <CGAL/compute_average_spacing.h>
+#include <CGAL/Delaunay_triangulation_2.h>
+#include <CGAL/Alpha_shape_2.h>
+#include <CGAL/Triangulation_face_base_with_info_2.h>
+#include <CGAL/Constrained_Delaunay_triangulation_2.h>
+
+#include <QObject>
+#include <QMenu>
+#include <QGLViewer/manipulatedCameraFrame.h>
+
+#include <set>
+#include <stack>
+#include <algorithm>
+#include <boost/array.hpp>
+
+Scene_point_set_classification_item::Scene_point_set_classification_item(PSC* psc)
+: Scene_item(NbOfVbos,NbOfVaos),
+  m_psc (psc),
+  m_scat (NULL),
+  m_elev (NULL),
+  m_hori (NULL),
+  m_plan (NULL),
+  m_colo (NULL)
+{
+  setRenderingMode(PointsPlusNormals);
+  m_index_color = 1;
+  is_selected = true;
+  nb_points = 0;
+  nb_lines = 0;
+}
+
+Scene_point_set_classification_item::Scene_point_set_classification_item(const Scene_points_with_normal_item* points,
+                                                                         double grid_resolution)
+  : Scene_item(NbOfVbos,NbOfVaos),
+    m_psc (NULL),
+    m_scat (NULL),
+    m_elev (NULL),
+    m_hori (NULL),
+    m_plan (NULL),
+    m_colo (NULL)
+{
+  setRenderingMode(PointsPlusNormals);
+  m_index_color = 1;
+  m_psc = new PSC(points->point_set()->begin(), points->point_set()->end(), grid_resolution);
+  
+  is_selected = true;
+  nb_points = 0;
+  nb_lines = 0;
+}
+
+
+// Copy constructor
+Scene_point_set_classification_item::Scene_point_set_classification_item(const Scene_point_set_classification_item&)
+  :Scene_item(NbOfVbos,NbOfVaos), // do not call superclass' copy constructor
+   m_psc (NULL),
+   m_scat (NULL),
+   m_elev (NULL),
+   m_hori (NULL),
+   m_plan (NULL),
+   m_colo (NULL)
+{
+  setRenderingMode(PointsPlusNormals);
+  m_index_color = 1;
+  
+  nb_points = 0;
+  invalidateOpenGLBuffers();
+}
+
+Scene_point_set_classification_item::~Scene_point_set_classification_item()
+{
+  if (m_psc != NULL)
+    delete m_psc;
+  if (m_scat != NULL)
+    delete m_scat;
+  if (m_elev != NULL)
+    delete m_elev;
+  if (m_hori != NULL)
+    delete m_hori;
+  if (m_plan != NULL)
+    delete m_plan;
+  if (m_colo != NULL)
+    delete m_colo;
+}
+
+void Scene_point_set_classification_item::initializeBuffers(CGAL::Three::Viewer_interface *viewer) const
+{
+  compute_normals_and_vertices();
+  //vao for the edges
+  {
+    program = getShaderProgram(PROGRAM_NO_SELECTION, viewer);
+    program->bind();
+
+    vaos[Edges]->bind();
+    buffers[Edges_vertices].bind();
+    buffers[Edges_vertices].allocate(positions_lines.data(),
+                                     static_cast<int>(positions_lines.size()*sizeof(double)));
+    program->enableAttributeArray("vertex");
+    program->setAttributeBuffer("vertex",GL_DOUBLE,0,3);
+    buffers[Edges_vertices].release();
+
+    vaos[Edges]->release();
+    nb_lines = positions_lines.size();
+    positions_lines.resize(0);
+    std::vector<double>(positions_lines).swap(positions_lines);
+    program->release();
+  }
+  //vao for the points
+  {
+    program = getShaderProgram(PROGRAM_NO_SELECTION, viewer);
+    program->bind();
+
+    vaos[ThePoints]->bind();
+    buffers[Points_vertices].bind();
+    buffers[Points_vertices].allocate(positions_points.data(),
+                                      static_cast<int>(positions_points.size()*sizeof(double)));
+    program->enableAttributeArray("vertex");
+    program->setAttributeBuffer("vertex",GL_DOUBLE,0,3);
+    buffers[Points_vertices].release();
+    if (!(colors_points.empty()))
+      {
+        buffers[Points_colors].bind();
+        buffers[Points_colors].allocate (colors_points.data(),
+                                         static_cast<int>(colors_points.size()*sizeof(double)));
+        program->enableAttributeArray("colors");
+        program->setAttributeBuffer("colors",GL_DOUBLE,0,3);
+        buffers[Points_colors].release();
+        std::vector<double>(colors_points).swap(colors_points);
+      }
+
+    vaos[ThePoints]->release();
+    nb_points = positions_points.size();
+    positions_points.resize(0);
+    std::vector<double>(positions_points).swap(positions_points);
+    program->release();
+  }
+  are_buffers_filled = true;
+}
+
+void Scene_point_set_classification_item::compute_normals_and_vertices() const
+{
+  int index_color = real_index_color();
+    
+  positions_lines.resize(0);
+  positions_points.resize(0);
+  colors_points.resize(0);
+  if (index_color == 9) // Show clusters centroids
+    {
+      positions_points.reserve(m_psc->clusters.size() * 3);
+      colors_points.reserve(m_psc->clusters.size() * 3);
+
+      for (std::size_t i = 0; i < m_psc->clusters.size(); ++ i)
+        {
+          const Kernel::Point_3& p = m_psc->clusters[i].centroid;
+          positions_points.push_back(p.x());
+          positions_points.push_back(p.y());
+          positions_points.push_back(p.z());
+
+          for (std::set<std::size_t>::iterator it = m_psc->clusters[i].neighbors.begin ();
+               it != m_psc->clusters[i].neighbors.end (); ++ it)
+            {
+              const Kernel::Point_3& q = m_psc->clusters[*it].centroid;
+              positions_lines.push_back(p.x());
+              positions_lines.push_back(p.y());
+              positions_lines.push_back(p.z());
+
+              positions_lines.push_back(q.x());
+              positions_lines.push_back(q.y());
+              positions_lines.push_back(q.z());
+            }
+        }
+    }
+  else // Show points
+    {
+      positions_points.reserve(m_psc->HPS.size() * 3);
+      colors_points.reserve(m_psc->HPS.size() * 3);
+
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          const Kernel::Point_3& p = m_psc->HPS[i].position;
+          positions_points.push_back(p.x());
+          positions_points.push_back(p.y());
+          positions_points.push_back(p.z());
+        }
+    }
+
+  // Colors
+  static Color_ramp ramp;
+  ramp.build_red();
+    
+
+  if (index_color == 0) // real colors
+    {
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          Color color = m_psc->HPS[i].color;
+
+          colors_points.push_back ((double)(color[0]) / 255.);
+          colors_points.push_back ((double)(color[1]) / 255.);
+          colors_points.push_back ((double)(color[2]) / 255.);
+        }
+    }
+  else if (index_color == 1) // classif
+    {
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          QColor color (0, 0, 0);
+          int c = m_psc->HPS[i].AE_label;
+          
+          if (c != -1)
+            {        
+              if (m_psc->segmentation_classes[c]->id() == "vegetation")
+                color = QColor(0, 255, 27);
+              else if (m_psc->segmentation_classes[c]->id() == "ground")
+                color = QColor(245, 180, 0);
+              else if (m_psc->segmentation_classes[c]->id() == "road")
+                color = QColor(114, 114, 130);
+              else if (m_psc->segmentation_classes[c]->id() == "roof")
+                color = QColor(255, 0, 170);
+              else if (m_psc->segmentation_classes[c]->id() == "facade")
+                color = QColor(100, 0, 255);
+              else if (m_psc->segmentation_classes[c]->id() == "building")
+                color = QColor(0, 114, 225);
+            }
+
+
+          colors_points.push_back ((double)(color.red()) / 255.);
+          colors_points.push_back ((double)(color.green()) / 255.);
+          colors_points.push_back ((double)(color.blue()) / 255.);
+        }
+    }
+  else if (index_color == 2) // confidence
+    {
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          QColor color (0, 0, 0);
+          int c = m_psc->HPS[i].AE_label;
+          
+          if (c != -1)
+            {        
+              if (m_psc->segmentation_classes[c]->id() == "vegetation")
+                color = QColor(0, 255, 27);
+              else if (m_psc->segmentation_classes[c]->id() == "ground")
+                color = QColor(245, 180, 0);
+              else if (m_psc->segmentation_classes[c]->id() == "road")
+                color = QColor(114, 114, 130);
+              else if (m_psc->segmentation_classes[c]->id() == "roof")
+                color = QColor(255, 0, 170);
+              else if (m_psc->segmentation_classes[c]->id() == "facade")
+                color = QColor(100, 0, 255);
+              else if (m_psc->segmentation_classes[c]->id() == "building")
+                color = QColor(0, 114, 225);
+            }
+
+          double confidence = m_psc->HPS[i].confidence;
+          colors_points.push_back (1. - confidence * (1. - (double)(color.red()) / 255.));
+          colors_points.push_back (1. - confidence * (1. - (double)(color.green()) / 255.));
+          colors_points.push_back (1. - confidence * (1. - (double)(color.blue()) / 255.));
+        }
+    }
+  else if (index_color == 3) // scatter
+    {
+      double weight = m_scat->weight;
+      m_scat->weight = m_scat->max;
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          colors_points.push_back (ramp.r(m_scat->value(i)));
+          colors_points.push_back (ramp.g(m_scat->value(i)));
+          colors_points.push_back (ramp.b(m_scat->value(i)));
+        }
+      m_scat->weight = weight;
+    }
+  else if (index_color == 4) // planarity
+    {
+      double weight = m_plan->weight;
+      m_plan->weight = m_plan->max;
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          colors_points.push_back (ramp.r(m_plan->value(i)));
+          colors_points.push_back (ramp.g(m_plan->value(i)));
+          colors_points.push_back (ramp.b(m_plan->value(i)));
+        }
+      m_plan->weight = weight;
+    }
+  else if (index_color == 5) // horizontality
+    {
+      double weight = m_hori->weight;
+      m_hori->weight = m_hori->max;
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          colors_points.push_back (ramp.r(m_hori->value(i)));
+          colors_points.push_back (ramp.g(m_hori->value(i)));
+          colors_points.push_back (ramp.b(m_hori->value(i)));
+        }
+      m_hori->weight = weight;
+    }
+  else if (index_color == 6) // elevation
+    {
+      double weight = m_elev->weight;
+      m_elev->weight = m_elev->max;
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          colors_points.push_back (ramp.r(m_elev->value(i)));
+          colors_points.push_back (ramp.g(m_elev->value(i)));
+          colors_points.push_back (ramp.b(m_elev->value(i)));
+        }
+      m_elev->weight = weight;
+    }
+  else if (index_color == 7) // color
+    {
+      double weight = m_colo->weight;
+      m_colo->weight = m_colo->max;
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          colors_points.push_back (ramp.r(m_colo->value(i)));
+          colors_points.push_back (ramp.g(m_colo->value(i)));
+          colors_points.push_back (ramp.b(m_colo->value(i)));
+        }
+      m_colo->weight = weight;
+    }
+  else if (index_color == 8) // RANSAC
+    {
+      int seed = time(NULL);
+        
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          if (m_psc->HPS[i].group == (std::size_t)(-1))
+            {
+              colors_points.push_back (0.);
+              colors_points.push_back (0.);
+              colors_points.push_back (0.);
+            }
+          else
+            {
+              srand (m_psc->HPS[i].group + seed);
+              colors_points.push_back (0.25 + 0.6 * (rand() / (double)RAND_MAX));
+              colors_points.push_back (0.25 + 0.6 * (rand() / (double)RAND_MAX));
+              colors_points.push_back (0.25 + 0.6 * (rand() / (double)RAND_MAX));
+            }
+        }
+    }
+  else if (index_color == 9) // Clusters
+    {
+      for (std::size_t i = 0; i < m_psc->clusters.size(); ++ i)
+        {
+          QColor color (0, 0, 0);
+          int c = m_psc->HPS[m_psc->clusters[i].indices[0]].AE_label;
+          
+          if (c != -1)
+            {        
+              if (m_psc->segmentation_classes[c]->id() == "vegetation")
+                color = QColor(0, 255, 27);
+              else if (m_psc->segmentation_classes[c]->id() == "ground")
+                color = QColor(245, 180, 0);
+              else if (m_psc->segmentation_classes[c]->id() == "road")
+                color = QColor(114, 114, 130);
+              else if (m_psc->segmentation_classes[c]->id() == "roof")
+                color = QColor(255, 0, 170);
+              else if (m_psc->segmentation_classes[c]->id() == "facade")
+                color = QColor(100, 0, 255);
+              else if (m_psc->segmentation_classes[c]->id() == "building")
+                color = QColor(0, 114, 225);
+            }
+
+
+          colors_points.push_back ((double)(color.red()) / 255.);
+          colors_points.push_back ((double)(color.green()) / 255.);
+          colors_points.push_back ((double)(color.blue()) / 255.);
+        }
+    }
+}
+
+
+// Duplicates scene item
+Scene_point_set_classification_item*
+Scene_point_set_classification_item::clone() const
+{
+  return new Scene_point_set_classification_item(*this);
+}
+
+// Loads point set from .PLY file
+bool Scene_point_set_classification_item::read_ply_point_set(std::istream& stream,
+                                                             double grid_resolution)
+{
+  CGAL::Timer timer;
+  timer.start();
+  std::vector<Kernel::Point_3> points;
+  std::vector<Color> colors;
+  std::vector<unsigned char> echo;
+
+  My_ply_interpreter<float> interpreter_f (points, colors, echo);
+
+  if (!CGAL::read_ply_custom_points (stream, interpreter_f, Kernel()))
+    {
+      std::cerr << "PLY reader with float not applicable." << std::endl;
+      stream.seekg(0);
+      My_ply_interpreter<double> interpreter_d (points, colors, echo);
+      if (!CGAL::read_ply_custom_points (stream, interpreter_d, Kernel()))
+        {
+          std::cerr << "PLY reader with double not applicable." << std::endl;
+          stream.seekg(0);
+          if (!CGAL::read_ply_points (stream, std::back_inserter (points)))
+            {
+              std::cerr << "Error: cannot read file " << std::endl;
+              return false;
+            }
+          m_psc = new PSC(points.begin(), points.end(), grid_resolution);
+          invalidateOpenGLBuffers();
+          return true;
+        }
+    }
+
+  m_psc = new PSC(points.begin(), points.end(), grid_resolution);
+
+  Color black = {{ 0, 0, 0 }};
+  
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    {
+      m_psc->HPS[i].color = colors[i];
+      if (colors[i] != black)
+        m_psc->has_colors = true;
+      m_psc->HPS[i].echo = echo[i];
+      if (echo[i] != 0)
+        m_psc->is_echo_given = true;
+    }
+
+  timer.stop();
+  std::cerr << "Reading took "<< timer.time() << "sec."<<std::endl;
+  if (m_psc->has_colors)
+    std::cerr << "Has colors" << std::endl;
+
+  invalidateOpenGLBuffers();
+  return true;
+}
+
+// Write point set to .PLY file
+bool Scene_point_set_classification_item::write_ply_point_set(std::ostream& stream) const
+{
+  if (m_psc->HPS[0].AE_label == (unsigned char)(-1))
+    {
+      std::cerr << "Error: classification was not performed." << std::endl;
+      return false;
+    }
+    
+  stream.precision (std::numeric_limits<double>::digits10 + 2);
+  
+  stream << "ply" << std::endl
+         << "format ascii 1.0" << std::endl
+         << "element vertex " << m_psc->HPS.size() << std::endl
+         << "property float x" << std::endl
+         << "property float y" << std::endl
+         << "property float z" << std::endl
+         << "property uchar red" << std::endl
+         << "property uchar green" << std::endl
+         << "property uchar blue" << std::endl
+         << "end_header" << std::endl;
+
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    {
+      QColor color (0, 0, 0);
+      int c = m_psc->HPS[i].AE_label;
+          
+      if (c != -1)
+        {        
+          if (m_psc->segmentation_classes[c]->id() == "vegetation")
+            color = QColor(0, 255, 27);
+          else if (m_psc->segmentation_classes[c]->id() == "ground")
+            color = QColor(245, 180, 0);
+          else if (m_psc->segmentation_classes[c]->id() == "road")
+            color = QColor(114, 114, 130);
+          else if (m_psc->segmentation_classes[c]->id() == "roof")
+            color = QColor(255, 0, 170);
+          else if (m_psc->segmentation_classes[c]->id() == "facade")
+            color = QColor(100, 0, 255);
+          else if (m_psc->segmentation_classes[c]->id() == "building")
+                      color = QColor(0, 114, 225);
+        }
+      
+      stream << m_psc->HPS[i].position << " "
+             << color.red() << " "
+             << color.green() << " "
+             << color.blue() << std::endl;
+    }
+    
+  return true;
+}
+
+QString
+Scene_point_set_classification_item::toolTip() const
+{
+  return QObject::tr("<p><b>%1</b><br />"
+                     "<i>Point set classification</i></p>"
+                     "<p>Number of points: %2</p>")
+    .arg(name())
+    .arg(m_psc->HPS.size());
+}
+
+bool Scene_point_set_classification_item::supportsRenderingMode(RenderingMode m) const 
+{
+  return m==PointsPlusNormals;
+
+}
+
+void Scene_point_set_classification_item::drawSplats(CGAL::Three::Viewer_interface*) const
+{
+}
+
+void Scene_point_set_classification_item::drawEdges(CGAL::Three::Viewer_interface* viewer) const
+{
+  if(!are_buffers_filled)
+    initializeBuffers(viewer);
+  vaos[Edges]->bind();
+  program=getShaderProgram(PROGRAM_NO_SELECTION);
+  attribBuffers(viewer,PROGRAM_NO_SELECTION);
+  program->bind();
+  program->setAttributeValue("colors", this->color());
+  viewer->glDrawArrays(GL_LINES, 0,
+                       static_cast<GLsizei>((nb_lines)/3));
+  vaos[Edges]->release();
+  program->release();
+}
+
+void Scene_point_set_classification_item::drawPoints(CGAL::Three::Viewer_interface* viewer) const
+{
+  if(!are_buffers_filled)
+    initializeBuffers(viewer);
+  GLfloat point_size;
+  viewer->glGetFloatv(GL_POINT_SIZE, &point_size);
+  if (m_index_color == 9)
+    viewer->glPointSize(15.f);
+  else
+    viewer->glPointSize(3.f);
+
+  vaos[ThePoints]->bind();
+  program=getShaderProgram(PROGRAM_NO_SELECTION);
+  attribBuffers(viewer,PROGRAM_NO_SELECTION);
+  program->bind();
+  if (colors_points.empty())
+    program->setAttributeValue("colors", this->color());
+  viewer->glDrawArrays(GL_POINTS, 0,
+                       static_cast<GLsizei>(((std::size_t)(nb_points)/3)));
+  vaos[ThePoints]->release();
+  program->release();
+  viewer->glPointSize(point_size);
+}
+
+bool
+Scene_point_set_classification_item::isEmpty() const
+{
+  return false;
+}
+
+void
+Scene_point_set_classification_item::compute_bbox() const
+{
+  if (m_psc == NULL)
+    return;
+  
+  double xmin = std::numeric_limits<double>::max();
+  double ymin = std::numeric_limits<double>::max();
+  double zmin = std::numeric_limits<double>::max();
+  double xmax = -std::numeric_limits<double>::max();
+  double ymax = -std::numeric_limits<double>::max();
+  double zmax = -std::numeric_limits<double>::max();
+
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    {
+      const Kernel::Point_3& p = m_psc->HPS[i].position;
+      xmin = (std::min)(xmin, p.x());
+      ymin = (std::min)(ymin, p.y());
+      zmin = (std::min)(zmin, p.z());
+      xmax = (std::max)(xmax, p.x());
+      ymax = (std::max)(ymax, p.y());
+      zmax = (std::max)(zmax, p.z());
+    }
+  
+  _bbox = Bbox(xmin,ymin,zmin,
+               xmax,ymax,zmax);
+}
+
+QMenu* Scene_point_set_classification_item::contextMenu()
+{
+  QMenu* menu = Scene_item::contextMenu();
+
+  return menu;
+}
+
+void Scene_point_set_classification_item::setRenderingMode(RenderingMode m)
+{
+  Scene_item::setRenderingMode(m);
+}
+
+void Scene_point_set_classification_item::invalidateOpenGLBuffers()
+{
+  are_buffers_filled = false;
+  compute_bbox();
+}
+
+void Scene_point_set_classification_item::change_color (int index)
+{
+  m_index_color = index;
+  invalidateOpenGLBuffers();
+}
+
+int Scene_point_set_classification_item::real_index_color() const
+{
+  int out = m_index_color;
+  
+  if (out == 1 && m_psc->HPS[0].AE_label == (unsigned char)(-1))
+    out = 0;
+
+  if (out == 3 && m_scat == NULL)
+    out = 0;
+  if (out == 4 && m_plan == NULL)
+    out = 0;
+  if (out == 5 && m_hori == NULL)
+    out = 0;
+  if (out == 6 && m_elev == NULL)
+    out = 0;
+  if (out == 7 && m_colo == NULL)
+    out = 0;
+
+  if (out == 0 && !(m_psc->has_colors))
+    out = -1;
+  return out;
+}
+
+void Scene_point_set_classification_item::estimate_parameters (double& grid_resolution,
+                                                               double& radius_neighbors,
+                                                               double& radius_dtm)
+{
+  std::vector<Kernel::Point_3> pts;
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    pts.push_back(m_psc->HPS[i].position);
+
+  double average_spacing = CGAL::compute_average_spacing<CGAL::Sequential_tag> (pts.begin(), pts.end(), 6);
+
+  grid_resolution = average_spacing;
+  radius_neighbors = 5 * grid_resolution;
+  radius_dtm = 5 * radius_neighbors;
+}
+
+void Scene_point_set_classification_item::compute_features (const double& grid_resolution,
+                                                            const double& radius_neighbors,
+                                                            const double& radius_dtm,
+                                                            const QColor& c)
+{
+  Q_ASSERT (m_psc != NULL);
+
+  bool on_groups = !(m_psc->groups.empty());
+  
+  m_psc->set_parameters (grid_resolution, radius_neighbors, radius_dtm);
+  m_psc->initialization();
+
+  if (m_scat != NULL) delete m_scat;
+  m_scat = new Scatter (*m_psc, 1., on_groups);
+  m_psc->segmentation_attributes.push_back (m_scat);
+  
+  if (m_plan != NULL) delete m_plan;
+  m_plan = new NonPlanarity (*m_psc, 1., on_groups);
+  m_psc->segmentation_attributes.push_back (m_plan);
+  
+  if (m_hori != NULL) delete m_hori;
+  m_hori = new Horizontality (*m_psc, 1., on_groups);
+  m_psc->segmentation_attributes.push_back (m_hori);
+
+  if (m_elev != NULL) delete m_elev;
+  m_elev = new Elevation (*m_psc, 1., on_groups);
+  m_psc->segmentation_attributes.push_back (m_elev);
+
+  if (m_colo != NULL) delete m_colo;
+  m_colo = new ColorSeg (*m_psc, 1., c.hue(), c.saturation(), c.value());
+  m_psc->segmentation_attributes.push_back (m_colo);
+
+}
+
+
+void Scene_point_set_classification_item::compute_ransac (const double& radius_neighbors)
+{
+  Q_ASSERT (m_psc != NULL);
+
+  std::cerr << "Computing normals..." << std::endl;
+
+  // Estimates normals direction.
+  std::vector<std::size_t> indices (m_psc->HPS.size());
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    indices[i] = i;
+
+  std::vector<Kernel::Vector_3> normals (m_psc->HPS.size(), CGAL::NULL_VECTOR);
+
+  HPS_property_map<PSC::HPoint> hps_pmap (&(m_psc->HPS));
+  CGAL::jet_estimate_normals<CGAL::Sequential_tag>(indices.begin(), indices.end(),
+                                                   hps_pmap,
+                                                   CGAL::make_property_map(normals),
+                                                   12);
+
+  typedef CGAL::Shape_detection_3::Efficient_RANSAC_traits<Kernel, std::vector<std::size_t>,
+                                                           HPS_property_map<PSC::HPoint>,
+                                                           typename CGAL::Pointer_property_map<Kernel::Vector_3>::type > Traits;
+  typedef CGAL::Shape_detection_3::Efficient_RANSAC<Traits> Shape_detection;
+
+  Shape_detection shape_detection;
+
+
+  shape_detection.set_input(indices, hps_pmap, CGAL::make_property_map(normals));
+  //  shape_detection.add_shape_factory<CGAL::Shape_detection_3::Plane<Traits> >();
+  shape_detection.add_shape_factory<My_plane<Traits> >();
+  Shape_detection::Parameters op;
+  op.probability = 0.05;
+  op.min_points = std::max ((std::size_t)10, m_psc->HPS.size() / 250000);
+  
+  op.epsilon = radius_neighbors;
+  op.cluster_epsilon = radius_neighbors;
+  op.normal_threshold = 0.9;
+
+  std::cerr << "Computing RANSAC..." << std::endl;
+  shape_detection.detect(op);
+
+  
+  CGAL::regularize_planes (shape_detection, true, true, true, false,
+                           10., radius_neighbors);
+  
+  m_psc->reset_groups();
+
+  std::cerr << "Storing groups..." << std::endl;
+  
+  BOOST_FOREACH(boost::shared_ptr<Shape_detection::Shape> shape, shape_detection.shapes())
+    {
+      m_psc->groups.push_back ((Kernel::Plane_3)(*(dynamic_cast<CGAL::Shape_detection_3::Plane<Traits>*>(shape.get ()))));
+      BOOST_FOREACH(std::size_t i, shape->indices_of_assigned_points())
+        m_psc->HPS[indices[i]].group = m_psc->groups.size() - 1;
+      
+    }
+  std::cerr << "Found " << m_psc->groups.size() << " group(s)" << std::endl;
+}
+
+
+void Scene_point_set_classification_item::compute_clusters (const double& radius_neighbors)
+{
+  Q_ASSERT (m_psc != NULL);
+
+  m_psc->cluster_points (radius_neighbors);
+
+  std::size_t index_ground = 0;
+  std::size_t index_roof = 0;
+  std::size_t index_facade = 0;
+  std::size_t index_vege = 0;
+  
+  for (std::size_t i = 0; i < m_psc->segmentation_classes.size(); ++ i)
+    if (m_psc->segmentation_classes[i]->id() == "ground")
+      index_ground = i;
+    else if (m_psc->segmentation_classes[i]->id() == "roof")
+      index_roof = i;
+    else if (m_psc->segmentation_classes[i]->id() == "facade")
+      index_facade = i;
+    else if (m_psc->segmentation_classes[i]->id() == "vegetation")
+      index_vege = i;
+
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    {
+      if (m_psc->HPS[i].neighbor == (unsigned char)(-1))
+        {
+          m_psc->HPS[i].confidence = 0.;
+          continue;
+        }
+
+      std::size_t c0 = m_psc->HPS[i].AE_label;
+      std::size_t c1 = m_psc->HPS[i].neighbor;
+      
+      if ((c0 == index_ground && c1 == index_facade) || (c0 == index_facade && c1 == index_ground) ||
+          (c0 == index_ground && c1 == index_vege) || (c0 == index_vege && c1 == index_ground) ||
+          (c0 == index_facade && c1 == index_roof) || (c0 == index_roof && c1 == index_facade))
+        m_psc->HPS[i].confidence = 1.;
+      else
+        m_psc->HPS[i].confidence = 0.2;
+    }
+  
+  // for (std::size_t i = 0; i < m_psc->clusters.size(); ++ i)
+  //   {
+  //     std::size_t c0 = m_psc->HPS[m_psc->clusters[i].indices[0]].AE_label;
+  //     std::size_t nb_good = 1;
+  //     std::size_t nb_tot = 1;
+      
+  //     for (std::set<std::size_t>::iterator it = m_psc->clusters[i].neighbors.begin ();
+  //          it != m_psc->clusters[i].neighbors.end (); ++ it)
+  //       {
+  //         std::size_t c1 = m_psc->HPS[m_psc->clusters[*it].indices[0]].AE_label;
+  //         std::size_t weight = 1;//std::min (m_psc->clusters[*it].indices.size(), m_psc->clusters[i].indices.size());
+  //         nb_tot += weight;
+          
+  //         if ((c0 == index_ground && c1 == index_facade) || (c0 == index_facade && c1 == index_ground) ||
+  //             (c0 == index_ground && c1 == index_vege) || (c0 == index_vege && c1 == index_ground) ||
+  //             (c0 == index_facade && c1 == index_roof) || (c0 == index_roof && c1 == index_facade))
+  //           nb_good += weight;
+  //       }
+      
+  //     double confidence = nb_good / (double)nb_tot;
+  //     for (std::size_t j = 0; j < m_psc->clusters[i].indices.size(); ++ j)
+  //       m_psc->HPS[m_psc->clusters[i].indices[j]].confidence = confidence;
+  //   }
+
+  invalidateOpenGLBuffers();
+}
+
+
+void Scene_point_set_classification_item::save_2d_image()
+{
+  std::ofstream f ("out.ppm");
+  f << "P3" << std::endl
+    << m_psc->grid_HPS.width() << " " << m_psc->grid_HPS.height() << std::endl
+    << "255" << std::endl;
+
+  std::size_t cnt = 0;
+  for (std::size_t i = 0; i < m_psc->grid_HPS.height(); ++ i)
+    for (std::size_t j = 0; j < m_psc->grid_HPS.width(); ++ j)
+      {
+        std::vector<std::size_t> scores (m_psc->segmentation_classes.size(), 0);
+
+        if (m_psc->grid_HPS(i,j).empty())
+          f << "255 255 255 ";
+        else
+          {
+            for (std::size_t k = 0; k < m_psc->grid_HPS(i,j).size(); ++ k)
+              scores[m_psc->HPS[m_psc->grid_HPS(i,j)[k]].AE_label] ++;
+
+            std::size_t max = 0;
+            std::size_t c = 0;
+            for (std::size_t k = 0; k < scores.size(); ++ k)
+              if (scores[k] > max)
+                {
+                  c = k;
+                  max = scores[k];
+                }
+        
+            if (m_psc->segmentation_classes[c]->id() == "vegetation")
+              f << "0 255 27 ";
+            else if (m_psc->segmentation_classes[c]->id() == "ground")
+              f << "245 180 0 ";
+            else if (m_psc->segmentation_classes[c]->id() == "road")
+              f << "114 114 130 ";
+            else if (m_psc->segmentation_classes[c]->id() == "roof")
+              f << "255 0 170 ";
+            else if (m_psc->segmentation_classes[c]->id() == "facade")
+              f << "100 0 255 ";
+            else if (m_psc->segmentation_classes[c]->id() == "building")
+              f << "0 114 255 ";
+            else
+              f << "255 255 255 ";
+          }
+        
+        ++ cnt;
+        if (cnt == 5)
+          {
+            f << std::endl;
+            cnt = 0;
+          }
+      }
+
+  f.close();
+}
+
+void Scene_point_set_classification_item::extract_2d_outline (double radius,
+                                                              std::vector<Kernel::Point_3>& outline)
+{
+    
+  // Projection
+  double mean_z = 0.;
+  std::size_t nb = 0;
+  std::vector<Kernel::Point_2> pts;
+  
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    if (m_psc->HPS[i].AE_label == 1)
+      {
+        mean_z += m_psc->HPS[i].position.z();
+        nb ++;
+      }
+    else if (m_psc->HPS[i].AE_label > 1)
+      pts.push_back (Kernel::Point_2 (m_psc->HPS[i].position.x(),
+                                      m_psc->HPS[i].position.y()));
+
+  mean_z /= nb;
+  
+
+  // Alpha shapes
+  typedef CGAL::Alpha_shape_vertex_base_2<Kernel> Vb;
+  typedef CGAL::Alpha_shape_face_base_2<Kernel>  Fb;
+  typedef CGAL::Triangulation_data_structure_2<Vb,Fb> Tds;
+  typedef CGAL::Delaunay_triangulation_2<Kernel,Tds> Triangulation_2;
+  typedef CGAL::Alpha_shape_2<Triangulation_2>  Alpha_shape_2;
+
+  Alpha_shape_2 ashape (pts.begin (), pts.end (), radius);
+
+  // Output
+  for (Alpha_shape_2::Finite_edges_iterator it = ashape.finite_edges_begin ();
+       it != ashape.finite_edges_end (); ++ it)
+    {
+      if (ashape.classify(*it) != Alpha_shape_2::REGULAR)
+        continue;
+
+      for (std::size_t i = 0; i < 2; ++ i)
+        {
+          const Kernel::Point_2& p = it->first->vertex ((it->second + 1 + i) % 3)->point();
+          outline.push_back (Kernel::Point_3 (p.x(), p.y(), mean_z));
+        }
+
+    }
+
+}
+
+void Scene_point_set_classification_item::extract_building_map (double,
+                                                                std::vector<Kernel::Triangle_3>& faces)
+{
+  std::size_t index_ground = (std::size_t)(-1);
+  std::size_t index_roof = (std::size_t)(-1);
+  std::size_t index_facade = (std::size_t)(-1);
+  for (std::size_t i = 0; i < m_psc->segmentation_classes.size(); ++ i)
+    if (m_psc->segmentation_classes[i]->id() == "ground")
+      index_ground = i;
+    else if (m_psc->segmentation_classes[i]->id() == "roof")
+      index_roof = i;
+    else if (m_psc->segmentation_classes[i]->id() == "facade")
+      index_facade = i;
+
+  bool separate_only = (index_roof == (std::size_t)(-1));
+
+  // Estimate ground Z
+  std::cerr << "Estimating ground..." << std::endl;
+  std::vector<double> z_ground;
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    if (m_psc->HPS[i].AE_label == index_ground)
+      z_ground.push_back (m_psc->HPS[i].position.z());
+
+  std::sort (z_ground.begin(), z_ground.end());
+  double z_median = z_ground[z_ground.size() / 2];
+  Kernel::Plane_3 ground (Kernel::Point_3 (0., 0., z_median), Kernel::Vector_3 (0., 0., 1.));
+  
+  // Project facade planes and get lines
+  std::cerr << "Projecting facade planes..." << std::endl;
+  std::vector<bool> is_plane_facade (m_psc->groups.size(), false);
+  std::vector<std::vector<Kernel::Point_3> > facade_pts (m_psc->groups.size());
+  
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    if (m_psc->HPS[i].AE_label == index_facade && m_psc->HPS[i].group != (std::size_t)(-1))
+      {
+        is_plane_facade[m_psc->HPS[i].group] = true;
+        facade_pts[m_psc->HPS[i].group].push_back (m_psc->HPS[i].position);
+      }
+
+
+  std::vector<Kernel::Segment_3> borders;
+  borders.push_back (Kernel::Segment_3 (Kernel::Point_3 (_bbox.xmin(), _bbox.ymin(), z_median),
+                                        Kernel::Point_3 (_bbox.xmin(), _bbox.ymax(), z_median)));
+  borders.push_back (Kernel::Segment_3 (Kernel::Point_3 (_bbox.xmin(), _bbox.ymax(), z_median),
+                                        Kernel::Point_3 (_bbox.xmax(), _bbox.ymax(), z_median)));
+  borders.push_back (Kernel::Segment_3 (Kernel::Point_3 (_bbox.xmax(), _bbox.ymax(), z_median),
+                                        Kernel::Point_3 (_bbox.xmax(), _bbox.ymin(), z_median)));
+  borders.push_back (Kernel::Segment_3 (Kernel::Point_3 (_bbox.xmax(), _bbox.ymin(), z_median),
+                                        Kernel::Point_3 (_bbox.xmin(), _bbox.ymin(), z_median)));
+
+  std::vector<Kernel::Segment_3> lines;
+  for (std::size_t i = 0; i < is_plane_facade.size(); ++ i)
+    if (is_plane_facade[i])
+      {
+        Kernel::Plane_3 plane = m_psc->groups[i];
+        Kernel::Vector_3 normal = plane.orthogonal_vector();
+        normal = normal / std::sqrt (normal * normal);
+        // double horiz = normal * Kernel::Vector_3 (0., 0., 1.);
+        // if (horiz > 0.17 || horiz < -0.17)
+        //   continue;
+
+        Kernel::Point_3 c = CGAL::centroid (facade_pts[i].begin(), facade_pts[i].end());
+        normal = Kernel::Vector_3 (normal.x(), normal.y(), 0.);
+        plane = Kernel::Plane_3 (c, normal);
+        
+        //#define INFINITE_LINES
+#if defined(INFINITE_LINES)
+
+        Kernel::Point_3 source, target;
+        bool source_found = false;
+
+        for (std::size_t j = 0; j < borders.size(); ++ j)
+          {
+            CGAL::Object obj = CGAL::intersection (borders[j], plane);
+            Kernel::Point_3 inter;
+            if (CGAL::assign (inter, obj))
+              {
+                if (source_found)
+                  {
+                    target = inter;
+                    lines.push_back (Kernel::Segment_3 (source, target));
+                    break;
+                  }
+                else
+                  {
+                    source = inter;
+                    source_found = true;
+                  }
+              }
+          }
+#else
+        CGAL::Object obj = CGAL::intersection (ground, plane);
+        Kernel::Line_3 line;
+
+        // if (facade_pts[i].size() < 100)
+        //   continue;
+        
+        if (CGAL::assign (line, obj))
+          {
+            Kernel::Point_3 origin = line.projection (CGAL::ORIGIN);
+            Kernel::Vector_3 v (line);
+            double min = std::numeric_limits<double>::max();
+            double max = -std::numeric_limits<double>::max();
+            Kernel::Point_3 source = CGAL::ORIGIN;
+            Kernel::Point_3 target = CGAL::ORIGIN;
+            for (std::size_t j = 0; j < facade_pts[i].size(); ++ j)
+              {
+                Kernel::Vector_3 dir (origin, facade_pts[i][j]);
+                double prod = v * dir;
+                if (prod > max)
+                  {
+                    max = prod;
+                    target = facade_pts[i][j];
+                  }
+                if (prod < min)
+                  {
+                    min = prod;
+                    source = facade_pts[i][j];
+                  }
+              }
+            source = line.projection (source);
+            target = line.projection (target);
+            Kernel::Point_3 middle (0.5 * (source.x() + target.x()),
+                                    0.5 * (source.y() + target.y()),
+                                    0.5 * (source.z() + target.z()));
+
+            source = middle + 1.1 * (source - middle);
+            target = middle + 1.1 * (target - middle);
+            lines.push_back (Kernel::Segment_3 (source, target));
+          }
+#endif
+      }
+
+  std::cerr << " -> Found " << lines.size() << " line(s)" << std::endl;
+  
+  // Build 2D constrained Delaunay triangulation
+  std::cerr << "Building 2D constrained Delaunay triangulation..." << std::endl;
+    
+  typedef CGAL::Triangulation_vertex_base_2<Kernel>                Vb;
+  typedef CGAL::Triangulation_face_base_with_info_2<int,Kernel>    Fbwi;
+  typedef CGAL::Constrained_triangulation_face_base_2<Kernel,Fbwi> Fb;
+  typedef CGAL::Triangulation_data_structure_2<Vb,Fb>              TDS;
+  typedef CGAL::Exact_predicates_tag                               Itag;
+  typedef CGAL::Constrained_Delaunay_triangulation_2<Kernel, TDS, Itag> CDT;
+
+  CDT cdt;
+  cdt.insert (Kernel::Point_2 (_bbox.xmin(), _bbox.ymin()));
+  cdt.insert (Kernel::Point_2 (_bbox.xmin(), _bbox.ymax()));
+  cdt.insert (Kernel::Point_2 (_bbox.xmax(), _bbox.ymin()));
+  cdt.insert (Kernel::Point_2 (_bbox.xmax(), _bbox.ymax()));
+  for (std::size_t i = 0; i < lines.size(); ++ i)
+    cdt.insert_constraint (Kernel::Point_2 (lines[i].source().x(), lines[i].source().y()),
+                           Kernel::Point_2 (lines[i].target().x(), lines[i].target().y()));
+
+  std::cerr << " -> " << cdt.number_of_faces () << " face(s) created" << std::endl;
+  if (separate_only)
+    {
+      std::vector<std::vector<double> > heights;
+      
+      for (CDT::Finite_faces_iterator it = cdt.finite_faces_begin(); it != cdt.finite_faces_end(); ++ it)
+        {
+          it->info() = heights.size();
+          heights.push_back (std::vector<double>());
+          
+        }
+
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        if (m_psc->HPS[i].AE_label == index_ground)
+          {
+            CDT::Face_handle f = cdt.locate (Kernel::Point_2 (m_psc->HPS[i].position.x(),
+                                                              m_psc->HPS[i].position.y()));
+
+            heights[f->info()].push_back (m_psc->HPS[i].position.z());
+          }
+      
+      std::vector<double> h;
+      for (std::size_t i = 0; i < heights.size(); ++ i)
+        {
+          if (heights[i].empty())
+            {
+              h.push_back (0.);
+              continue;
+            }
+          std::sort (heights[i].begin(), heights[i].end());
+          h.push_back (heights[i][heights[i].size() / 10]);
+        }
+      
+      for (CDT::Finite_faces_iterator it = cdt.finite_faces_begin(); it != cdt.finite_faces_end(); ++ it)
+        {
+          faces.push_back (Kernel::Triangle_3 (Kernel::Point_3 (it->vertex (0)->point().x(),
+                                                                it->vertex (0)->point().y(),
+                                                                h[it->info()]),
+                                               Kernel::Point_3 (it->vertex (1)->point().x(),
+                                                                it->vertex (1)->point().y(),
+                                                                h[it->info()]),
+                                               Kernel::Point_3 (it->vertex (2)->point().x(),
+                                                                it->vertex (2)->point().y(),
+                                                                h[it->info()])));
+        }
+
+      for (CDT::Finite_edges_iterator it = cdt.finite_edges_begin(); it != cdt.finite_edges_end(); ++ it)
+        {
+          if (cdt.is_infinite (it->first) ||
+              cdt.is_infinite (it->first->neighbor(it->second)))
+            continue;
+          Kernel::Point_3 a (it->first->vertex ((it->second + 1)%3)->point().x(),
+                             it->first->vertex ((it->second + 1)%3)->point().y(),
+                             h[it->first->info()]);
+          Kernel::Point_3 b (it->first->vertex ((it->second + 1)%3)->point().x(),
+                             it->first->vertex ((it->second + 1)%3)->point().y(),
+                             h[it->first->neighbor(it->second)->info()]);
+          Kernel::Point_3 c (it->first->vertex ((it->second + 2)%3)->point().x(),
+                             it->first->vertex ((it->second + 2)%3)->point().y(),
+                             h[it->first->info()]);
+          Kernel::Point_3 d (it->first->vertex ((it->second + 2)%3)->point().x(),
+                             it->first->vertex ((it->second + 2)%3)->point().y(),
+                             h[it->first->neighbor(it->second)->info()]);
+          faces.push_back (Kernel::Triangle_3 (a, b, c));
+          faces.push_back (Kernel::Triangle_3 (b, c, d));
+        }
+    }
+  else
+    {
+      for (CDT::Finite_faces_iterator it = cdt.finite_faces_begin(); it != cdt.finite_faces_end(); ++ it)
+        it->info() = -10;
+
+      // Project ground + roof points on faces
+      std::cerr << "Projecting ground and roof points on faces..." << std::endl;
+      for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+        {
+          int iter = 0;
+          if (m_psc->HPS[i].AE_label == index_roof)
+            iter = 1;
+          else if (m_psc->HPS[i].AE_label == index_ground)
+            iter = -1;
+          else
+            continue;
+
+          CDT::Face_handle f = cdt.locate (Kernel::Point_2 (m_psc->HPS[i].position.x(),
+                                                            m_psc->HPS[i].position.y()));
+
+          f->info() += iter;
+        }
+
+  
+      // Label and extract faces
+      for (CDT::Finite_faces_iterator it = cdt.finite_faces_begin(); it != cdt.finite_faces_end(); ++ it)
+        if (it->info() > 0)
+          faces.push_back (Kernel::Triangle_3 (Kernel::Point_3 (it->vertex (0)->point().x(),
+                                                                it->vertex (0)->point().y(),
+                                                                z_median),
+                                               Kernel::Point_3 (it->vertex (1)->point().x(),
+                                                                it->vertex (1)->point().y(),
+                                                                z_median),
+                                               Kernel::Point_3 (it->vertex (2)->point().x(),
+                                                                it->vertex (2)->point().y(),
+                                                                z_median)));
+      std::cerr << " -> Found " << faces.size() << " building face(s)" << std::endl;
+    }
+}
+
+
+void Scene_point_set_classification_item::extract_facades (double radius,
+                                                           std::vector<Kernel::Triangle_3>& faces)
+{
+  std::size_t index_facade = 0;
+  for (std::size_t i = 0; i < m_psc->segmentation_classes.size(); ++ i)
+    if (m_psc->segmentation_classes[i]->id() == "facade")
+      {
+        index_facade = i;
+        break;
+      }
+
+  typedef CGAL::Alpha_shape_vertex_base_2<Kernel> Vb;
+  typedef CGAL::Alpha_shape_face_base_2<Kernel>  Fb;
+  typedef CGAL::Triangulation_data_structure_2<Vb,Fb> Tds;
+  typedef CGAL::Delaunay_triangulation_2<Kernel,Tds> Triangulation_2;
+  typedef CGAL::Alpha_shape_2<Triangulation_2>  Alpha_shape_2;
+  
+  std::vector<std::vector<Kernel::Point_3> > facade_pts (m_psc->groups.size());
+    
+  for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+    if (m_psc->HPS[i].AE_label == index_facade && m_psc->HPS[i].group != (std::size_t)(-1))
+      {
+        facade_pts[m_psc->HPS[i].group].push_back (m_psc->HPS[i].position);
+      }
+
+  for (std::size_t i = 0; i < facade_pts.size(); ++ i)
+    {
+      Kernel::Plane_3 plane = m_psc->groups[i];
+      Kernel::Vector_3 normal = plane.orthogonal_vector();
+      Kernel::Point_3 c = CGAL::centroid (facade_pts[i].begin(), facade_pts[i].end());
+      normal = Kernel::Vector_3 (normal.x(), normal.y(), 0.);
+      plane = Kernel::Plane_3 (c, normal);
+
+      std::vector<Kernel::Point_2> projections;
+      projections.reserve (facade_pts[i].size ());
+
+      for (std::size_t j = 0; j < facade_pts[i].size (); ++ j)
+        projections.push_back (plane.to_2d (facade_pts[i][j]));
+
+      Alpha_shape_2 ashape (projections.begin (), projections.end (), radius);
+  
+      for (Alpha_shape_2::Finite_faces_iterator it = ashape.finite_faces_begin ();
+           it != ashape.finite_faces_end (); ++ it)
+        {
+          if (ashape.classify (it) != Alpha_shape_2::INTERIOR)
+            continue;
+          faces.push_back (Kernel::Triangle_3 (plane.to_3d (it->vertex(0)->point()),
+                                               plane.to_3d (it->vertex(1)->point()),
+                                               plane.to_3d (it->vertex(2)->point())));
+        }
+    }
+}
diff --git a/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item.h b/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item.h
new file mode 100644
index 00000000000..3a91e9dbb8f
--- /dev/null
+++ b/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item.h
@@ -0,0 +1,648 @@
+#ifndef POINT_SET_CLASSIFICATION_ITEM_H
+#define POINT_SET_CLASSIFICATION_ITEM_H
+
+#include <CGAL/Three/Scene_item.h>
+#include <CGAL/IO/read_ply_points.h>
+#include <CGAL/Point_set_classification.h>
+#include <CGAL/Data_classification/Segmentation_attribute_scatter.h>
+#include <CGAL/Data_classification/Segmentation_attribute_elevation.h>
+#include <CGAL/Data_classification/Segmentation_attribute_horizontality.h>
+#include <CGAL/Data_classification/Segmentation_attribute_nonplanarity.h>
+#include <CGAL/Data_classification/Segmentation_attribute_color.h>
+#include <CGAL/Shape_detection_3/Shape_base.h>
+
+#include "Scene_point_set_classification_item_config.h"
+#include "Scene_points_with_normal_item.h"
+#include "Polyhedron_type_fwd.h"
+#include "Kernel_type.h"
+#include "Point_set_3.h"
+
+#include <iostream>
+
+typedef CGAL::Point_set_classification<Kernel> PSC;
+
+typedef CGAL::Segmentation_attribute_scatter<Kernel> Scatter;
+typedef CGAL::Segmentation_attribute_elevation<Kernel> Elevation;
+typedef CGAL::Segmentation_attribute_horizontality<Kernel> Horizontality;
+typedef CGAL::Segmentation_attribute_nonplanarity<Kernel> NonPlanarity;
+typedef CGAL::Segmentation_attribute_color<Kernel> ColorSeg;
+
+typedef CGAL::Data_classification::RGB_Color Color;
+
+class QMenu;
+class QAction;
+
+// This class represents a point set in the OpenGL scene
+class SCENE_POINT_SET_CLASSIFICATION_ITEM_EXPORT Scene_point_set_classification_item
+  : public CGAL::Three::Scene_item
+{
+  Q_OBJECT
+
+public:
+  
+  Scene_point_set_classification_item(PSC* psc = NULL);
+  Scene_point_set_classification_item(const Scene_points_with_normal_item* points,
+                                      double grid_resolution);
+  Scene_point_set_classification_item(const Scene_point_set_classification_item& toCopy);
+  ~Scene_point_set_classification_item();
+  
+  Scene_point_set_classification_item* clone() const;
+
+  struct Region
+  {
+    std::vector<std::size_t> indices;
+    double x_min, x_max, y_min, y_max;
+    Region () : indices(),
+                x_min(std::numeric_limits<double>::max()),
+                x_max(-std::numeric_limits<double>::max()),
+                y_min(std::numeric_limits<double>::max()),
+                y_max(-std::numeric_limits<double>::max()) { }
+  };
+
+  template <bool use_y>
+  struct Sort_by_coordinate
+  {
+    std::vector<PSC::HPoint>& m_hps;
+    Sort_by_coordinate (std::vector<PSC::HPoint>& hps) : m_hps (hps) { }
+    bool operator() (const std::size_t& a, const std::size_t& b)
+    {
+      if (use_y)
+        return m_hps[a].position.y() < m_hps[b].position.y();
+      else
+        return m_hps[a].position.x() < m_hps[b].position.x();
+    }
+  };
+  
+  template <typename OutputIterator>
+  bool segment_point_set (std::size_t nb_max_pt, OutputIterator output)
+  {
+    if (m_psc->HPS.size() < nb_max_pt)
+      return false;
+
+    std::list<Region> queue;
+    queue.push_front (Region());
+    
+    for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+      {
+        queue.front().indices.push_back (i);
+        if (m_psc->HPS[i].position.x() < queue.front().x_min)
+          queue.front().x_min = m_psc->HPS[i].position.x();
+        if (m_psc->HPS[i].position.x() > queue.front().x_max)
+          queue.front().x_max = m_psc->HPS[i].position.x();
+        if (m_psc->HPS[i].position.y() < queue.front().y_min)
+          queue.front().y_min = m_psc->HPS[i].position.y();
+        if (m_psc->HPS[i].position.y() > queue.front().y_max)
+          queue.front().y_max = m_psc->HPS[i].position.y();
+      }
+
+    while (!(queue.empty()))
+      {
+        Region& current = queue.front();
+
+        if (current.indices.size() < nb_max_pt)
+          {
+            std::vector<Kernel::Point_3> dummy;
+
+            PSC* psc = new PSC (dummy.begin(), dummy.end(), m_psc->m_grid_resolution);
+            for (std::size_t i = 0; i < current.indices.size(); ++ i)
+              {
+                psc->HPS.push_back (PSC::HPoint());
+                psc->HPS.back().position = m_psc->HPS[current.indices[i]].position;
+                psc->HPS.back().echo = m_psc->HPS[current.indices[i]].echo;
+                psc->HPS.back().ind_x = m_psc->HPS[current.indices[i]].ind_x;
+                psc->HPS.back().ind_y = m_psc->HPS[current.indices[i]].ind_y;
+                psc->HPS.back().group = m_psc->HPS[current.indices[i]].group;
+                psc->HPS.back().AE_label = m_psc->HPS[current.indices[i]].AE_label;
+                psc->HPS.back().neighbor = m_psc->HPS[current.indices[i]].neighbor;
+                psc->HPS.back().confidence = m_psc->HPS[current.indices[i]].confidence;
+                psc->HPS.back().color = m_psc->HPS[current.indices[i]].color;
+              }
+            *(output ++) = new Scene_point_set_classification_item (psc);
+          }
+        else
+          {
+            if (current.x_max - current.x_min > current.y_max - current.y_min)
+              {
+                std::sort (current.indices.begin(), current.indices.end(),
+                           Sort_by_coordinate<false>(m_psc->HPS));
+                
+                queue.push_back(Region());
+                queue.push_back(Region());
+                std::list<Region>::iterator it = queue.end();
+                Region& positive = *(-- it);
+                Region& negative = *(-- it);
+                
+                negative.y_min = current.y_min;
+                positive.y_min = current.y_min;
+                negative.y_max = current.y_max;
+                positive.y_max = current.y_max;
+                
+                negative.x_min = current.x_min;
+                positive.x_max = current.x_max;
+                std::size_t i = 0;
+                
+                for (; i < current.indices.size() / 2; ++ i)
+                  negative.indices.push_back (current.indices[i]);
+                double med_x = 0.5 * (m_psc->HPS[current.indices[i]].position.x()
+                                      + m_psc->HPS[current.indices[i+1]].position.x());
+                negative.x_max = med_x;
+                positive.x_min = med_x;
+                
+                for (; i < current.indices.size(); ++ i)
+                  positive.indices.push_back (current.indices[i]);
+              }
+            else
+              {
+                std::sort (current.indices.begin(), current.indices.end(),
+                           Sort_by_coordinate<true>(m_psc->HPS));
+
+                queue.push_back(Region());
+                queue.push_back(Region());
+                std::list<Region>::iterator it = queue.end();
+                Region& positive = *(-- it);
+                Region& negative = *(-- it);
+
+                negative.x_min = current.x_min;
+                positive.x_min = current.x_min;
+                negative.x_max = current.x_max;
+                positive.x_max = current.x_max;
+
+                negative.y_min = current.y_min;
+                positive.y_max = current.y_max;
+                std::size_t i = 0;
+                
+                for (; i < current.indices.size() / 2; ++ i)
+                  negative.indices.push_back (current.indices[i]);
+                double med_y = 0.5 * (m_psc->HPS[current.indices[i]].position.y()
+                                      + m_psc->HPS[current.indices[i+1]].position.y());
+                negative.y_max = med_y;
+                positive.y_min = med_y;
+                
+                for (; i < current.indices.size(); ++ i)
+                  positive.indices.push_back (current.indices[i]);
+              }
+          }
+        
+        queue.pop_front ();
+      }
+    
+    return true;
+  }
+  
+  // Function to override the context menu
+  QMenu* contextMenu();
+
+  // IO
+  bool read_ply_point_set(std::istream& in, double grid_resolution);
+  bool write_ply_point_set(std::ostream& out) const;
+  // Function for displaying meta-data of the item
+  virtual QString toolTip() const;
+
+  virtual void invalidateOpenGLBuffers();
+
+  // Indicate if rendering mode is supported
+  virtual bool supportsRenderingMode(RenderingMode m) const;
+
+  virtual void drawEdges(CGAL::Three::Viewer_interface* viewer) const;
+  virtual void drawPoints(CGAL::Three::Viewer_interface*) const;
+  virtual void drawSplats(CGAL::Three::Viewer_interface*) const;
+  
+
+  // Gets dimensions
+  virtual bool isFinite() const { return true; }
+  virtual bool isEmpty() const;
+  virtual void compute_bbox() const;
+
+  virtual void setRenderingMode(RenderingMode m);
+
+  void change_color (int index);
+  int real_index_color() const;
+  
+  void estimate_parameters (double& grid_resolution,
+                            double& radius_neighbors,
+                            double& radius_dtm);
+  void compute_features (const double& grid_resolution,
+                         const double& radius_neighbors,
+                         const double& radius_dtm,
+                         const QColor& c);
+  void compute_ransac (const double& radius_neighbors);
+  void compute_clusters (const double& radius_neighbors);
+
+  void save_2d_image ();
+  
+  template <typename Classes>
+  bool run (double weight_scat, double weight_plan,
+            double weight_hori, double weight_elev, double weight_colo,
+            bool multiply, Classes& classes, int method, double weight)
+  {
+  if (m_scat == NULL || m_plan == NULL || m_hori == NULL || m_elev == NULL || m_colo == NULL)
+    return false;
+
+  m_psc->m_grid_resolution = weight;
+
+  m_scat->weight = std::tan ((1. - weight_scat) * (CGAL_PI/2));
+  m_plan->weight = std::tan ((1. - weight_plan) * (CGAL_PI/2));
+  m_hori->weight = std::tan ((1. - weight_hori) * (CGAL_PI/2));
+  m_elev->weight = std::tan ((1. - weight_elev) * (CGAL_PI/2));
+  m_colo->weight = std::tan ((1. - weight_colo) * (CGAL_PI/2));
+  
+  // m_scat->weight = 2 * (1. - weight_scat) * m_scat->max;
+  // m_plan->weight = 2 * (1. - weight_plan) * m_plan->max;
+  // m_hori->weight = 2 * (1. - weight_hori) * m_hori->max;
+  // m_elev->weight = 2 * (1. - weight_elev) * m_elev->mean;
+  // m_colo->weight = 2 * (1. - weight_colo) * m_colo->max;
+
+  
+  if (!(m_psc->segmentation_classes.empty()))
+    {
+      for (std::size_t i = 0; i < m_psc->segmentation_classes.size(); ++ i)
+        delete m_psc->segmentation_classes[i];
+      m_psc->segmentation_classes.clear();
+    }
+
+  std::cerr << "Running with:" << std::endl;
+  for (std::size_t i = 0; i < classes.size(); ++ i)
+    {
+      if (!(classes[i].checkbox->isChecked()))
+        continue;
+      
+      m_psc->segmentation_classes.push_back (new CGAL::Classification_type
+                                             (classes[i].label->text().toLower().toStdString().c_str()));
+      m_psc->segmentation_classes.back()->set_attribute_effect (m_scat, (CGAL::Classification_type::Attribute_side)(classes[i].combo[0]->currentIndex()));
+      m_psc->segmentation_classes.back()->set_attribute_effect (m_plan, (CGAL::Classification_type::Attribute_side)(classes[i].combo[1]->currentIndex()));
+      m_psc->segmentation_classes.back()->set_attribute_effect (m_hori, (CGAL::Classification_type::Attribute_side)(classes[i].combo[2]->currentIndex()));
+      m_psc->segmentation_classes.back()->set_attribute_effect (m_elev, (CGAL::Classification_type::Attribute_side)(classes[i].combo[3]->currentIndex()));
+      m_psc->segmentation_classes.back()->set_attribute_effect (m_colo, (CGAL::Classification_type::Attribute_side)(classes[i].combo[4]->currentIndex()));
+
+      std::cerr << " * ";
+      m_psc->segmentation_classes.back()->info();
+    }
+  
+
+  std::cerr << "Weights: " << m_scat->weight << " " << m_plan->weight << " " << m_hori->weight
+            << " " << m_elev->weight << " " << m_colo->weight << std::endl;
+
+  m_psc->m_multiplicative = multiply;
+  
+  m_psc->classify(method);
+
+  // if (method != 0)
+  //   save_2d_image();
+  
+  invalidateOpenGLBuffers();
+  return false;
+}
+
+  template <typename ItemContainer>
+  void generate_point_sets (ItemContainer& items)
+  {
+    for (std::size_t i = 0; i < m_psc->HPS.size(); ++ i)
+      {
+        int c = m_psc->HPS[i].AE_label;
+        if (c == -1)
+          continue;
+        
+        if (m_psc->segmentation_classes[c]->id() == "vegetation")
+          items[0]->point_set()->push_back (m_psc->HPS[i].position);
+        else if (m_psc->segmentation_classes[c]->id() == "ground")
+          items[1]->point_set()->push_back (m_psc->HPS[i].position);
+        else if (m_psc->segmentation_classes[c]->id() == "road")
+          items[2]->point_set()->push_back (m_psc->HPS[i].position);
+        else if (m_psc->segmentation_classes[c]->id() == "roof")
+          items[3]->point_set()->push_back (m_psc->HPS[i].position);
+        else if (m_psc->segmentation_classes[c]->id() == "facade")
+          items[4]->point_set()->push_back (m_psc->HPS[i].position);
+        else if (m_psc->segmentation_classes[c]->id() == "building")
+          items[5]->point_set()->push_back (m_psc->HPS[i].position);
+      }
+
+  }
+
+  void extract_2d_outline (double radius,
+                           std::vector<Kernel::Point_3>& outline);
+  void extract_building_map (double radius,
+                             std::vector<Kernel::Triangle_3>& faces);
+  void extract_facades (double radius,
+                        std::vector<Kernel::Triangle_3>& faces);
+
+                                   
+public Q_SLOTS:
+
+// Data
+private:
+  PSC* m_psc;
+  Scatter* m_scat;
+  Elevation* m_elev;
+  Horizontality* m_hori;
+  NonPlanarity* m_plan;
+  ColorSeg* m_colo;
+  std::vector<Color> m_real_colors;
+
+  int m_index_color;
+  
+  enum VAOs {
+      Edges=0,
+      ThePoints,
+      NbOfVaos = ThePoints+1
+  };
+  enum VBOs {
+    Edges_vertices = 0,
+    Points_vertices,
+    Points_colors,
+    NbOfVbos = Points_colors+1
+  };
+
+  mutable std::vector<double> positions_lines;
+  mutable std::vector<double> positions_points;
+  mutable std::vector<double> colors_points;
+  mutable std::size_t nb_points;
+  mutable std::size_t nb_lines;
+   
+
+  mutable QOpenGLShaderProgram *program;
+
+  using CGAL::Three::Scene_item::initializeBuffers;
+  void initializeBuffers(CGAL::Three::Viewer_interface *viewer) const;
+
+  void compute_normals_and_vertices() const;
+
+
+}; // end class Scene_point_set_classification_item
+
+
+template <typename Floating>
+class My_ply_interpreter
+{
+  std::vector<Kernel::Point_3>& points;
+  std::vector<Color>& colors;
+  std::vector<unsigned char>& echo;
+  bool echo_float;
+  
+public:
+  My_ply_interpreter (std::vector<Kernel::Point_3>& points,
+                      std::vector<Color>& colors,
+                      std::vector<unsigned char>& echo)
+    : points (points), colors (colors), echo (echo), echo_float (false)
+  { }
+
+  // Init and test if input file contains the right properties
+  bool is_applicable (CGAL::Ply_reader& reader)
+  {
+    if (reader.does_tag_exist<float> ("scalar_Return_Number"))
+      echo_float = true;
+    
+    return reader.does_tag_exist<Floating> ("x")
+      && reader.does_tag_exist<Floating> ("y")
+      && reader.does_tag_exist<Floating> ("z");
+  }
+
+  // Describes how to process one line (= one point object)
+  void process_line (CGAL::Ply_reader& reader)
+  {
+    Floating x = (Floating)0., y = (Floating)0., z = (Floating)0.;
+    Color c = {{ 0, 0, 0 }};
+    unsigned char e = 0;
+
+    reader.assign (x, "x");
+    reader.assign (y, "y");
+    reader.assign (z, "z");
+    reader.assign (c[0], "red");
+    reader.assign (c[1], "green");
+    reader.assign (c[2], "blue");
+    if (echo_float)
+      {
+        float f = 0.;
+        reader.assign (f, "scalar_Return_Number");
+        e = (unsigned char)f;
+      }
+    else
+      reader.assign (e, "echo");
+
+    points.push_back (Kernel::Point_3 (x, y, z));
+    colors.push_back (c);
+    echo.push_back (e);
+  }
+
+};
+
+template <typename HPS>
+struct HPS_property_map{
+  const std::vector<HPS>* points;
+
+  typedef Kernel::Point_3 value_type;
+  typedef const value_type& reference;
+  typedef std::size_t key_type;
+  typedef boost::lvalue_property_map_tag category;
+  
+  HPS_property_map () : points(NULL)
+  {
+  }
+  HPS_property_map (std::vector<HPS>* pts) : points(pts)
+  {
+  }
+  ~HPS_property_map()
+  {
+  }
+
+  HPS_property_map (const HPS_property_map& pmap) : points(pmap.points)
+  {
+  }
+  
+  HPS_property_map& operator= (const HPS_property_map& pmap)
+  {
+    this->points = pmap.points;
+    return *this;
+  }
+  
+  reference operator[](key_type k) const
+  {
+    return (*points)[k].position;
+  }
+  
+  friend reference get(const HPS_property_map& ppmap,key_type i)
+  {return ppmap[i];}
+};
+
+
+
+
+
+template <class Traits>
+class My_plane : public CGAL::Shape_detection_3::Shape_base<Traits> {
+public:
+  /// \cond SKIP_IN_MANUAL
+  typedef typename Traits::Point_map Point_map;
+  ///< property map to access the location of an input point.
+  typedef typename Traits::Normal_map Normal_map;
+  ///< property map to access the unoriented normal of an input point.
+  typedef typename Traits::FT FT; ///< number type.
+  typedef typename Traits::Point_3 Point_3; ///< point type.
+  typedef typename Traits::Vector_3 Vector_3;
+  /// \endcond
+
+  typedef typename Traits::Plane_3 Plane_3;///< plane type for conversion operator.
+
+public:
+  My_plane() : CGAL::Shape_detection_3::Shape_base<Traits>() {}
+
+  /*!
+    Conversion operator to `Plane_3` type.
+  */
+  operator Plane_3() const {
+    return Plane_3(this->get_x(m_normal), this->get_y(m_normal), this->get_z(m_normal), m_d);
+  }
+            
+  /*!
+    Normal vector of the plane.
+  */
+  Vector_3 plane_normal() const {
+    return m_normal;
+  }
+            
+  /*!
+    Signed distance from the origin.
+  */
+  FT d() const {
+    return m_d;
+  }
+    
+  /// \cond SKIP_IN_MANUAL
+  /*!
+    Computes squared Euclidean distance from query point to the shape.
+  */
+  FT squared_distance(const Point_3 &p) const {
+    FT d = this->scalar_pdct(
+                             this->constr_vec(p, m_point_on_primitive), m_normal);
+    return d * d;
+  }
+
+    
+  /*!
+    Helper function to write the plane equation and
+    number of assigned points into a string.
+  */
+  std::string info() const {
+    std::stringstream sstr;
+    sstr << "Type: plane (" << this->get_x(m_normal) << ", " << this->get_y(m_normal) 
+         << ", " << this->get_z(m_normal) << ")x - " << m_d << "= 0"
+         << " #Pts: " << this->m_indices.size();
+
+    return sstr.str();
+  }
+  /// \endcond
+
+protected:
+  /// \cond SKIP_IN_MANUAL
+  virtual void create_shape(const std::vector<std::size_t> &indices) {
+    Point_3 p1 = this->point(indices[0]);
+    Point_3 p2 = this->point(indices[1]);
+    Point_3 p3 = this->point(indices[2]);
+
+    m_normal = this->cross_pdct(
+                                this->constr_vec(p2, p1), this->constr_vec(p3, p1));
+
+    if (std::fabs (m_normal * Vector_3 (0., 0., 1.)) > 0.5)
+      m_normal = Vector_3 (0., 0., 1.);
+    else
+      m_normal = Vector_3 (m_normal.x(), m_normal.y(), 0.);
+    
+    FT length = CGAL::sqrt(this->sqlen(m_normal));
+
+    // Are the points almost singular?
+    if (length < (FT)0.0001) {
+      return;
+    }
+
+    m_normal = this->scale(m_normal, (FT)1.0 / length);
+    m_d = -(this->get_x(p1) * this->get_x(m_normal) 
+            + this->get_y(p1) * this->get_y(m_normal) 
+            + this->get_z(p1) * this->get_z(m_normal));
+
+    //check deviation of the 3 normal
+    Vector_3 l_v = this->constr_vec(); 
+    for (std::size_t i = 0;i<3;i++) {
+      l_v = this->normal(indices[i]);
+
+      if (CGAL::abs(this->scalar_pdct(l_v, m_normal))
+          < this->m_normal_threshold * CGAL::sqrt(this->sqlen(l_v))) {
+        this->m_is_valid = false;
+        return;
+      }
+
+      m_point_on_primitive = p1;
+      m_base1 = this->cross_pdct(this->constr_vec(p2, p1), m_normal);
+      m_base1 = this->scale(m_base1, ((FT)1.0 / CGAL::sqrt(this->sqlen(m_base1))));
+
+      m_base2 = this->cross_pdct(m_base1, m_normal);
+      m_base2 = this->scale(m_base2, ((FT)1.0 / CGAL::sqrt(this->sqlen(m_base2))));
+    }
+
+    this->m_is_valid = true;
+  }
+
+  virtual void parameters(const std::vector<std::size_t> &indices,
+                          std::vector<std::pair<FT, FT> > &parameterSpace,
+                          FT &,                    
+                          FT min[2],
+                          FT max[2]) const {
+    // Transform first point before to initialize min/max
+    Vector_3 p = this->constr_vec(
+                                  m_point_on_primitive, this->point(indices[0]));
+    FT u = this->scalar_pdct(p, m_base1);
+    FT v = this->scalar_pdct(p, m_base2);
+    parameterSpace[0] = std::pair<FT, FT>(u, v);
+    min[0] = max[0] = u;
+    min[1] = max[1] = v;
+
+    for (std::size_t i = 1;i<indices.size();i++) {
+      p = this->constr_vec(m_point_on_primitive, this->point(indices[i]));
+      u = this->scalar_pdct(p, m_base1);
+      v = this->scalar_pdct(p, m_base2);
+      min[0] = (std::min<FT>)(min[0], u);
+      max[0] = (std::max<FT>)(max[0], u);
+      min[1] = (std::min<FT>)(min[1], v);
+      max[1] = (std::max<FT>)(max[1], v);
+      parameterSpace[i] = std::pair<FT, FT>(u, v);
+    }
+  }
+    
+  virtual void squared_distance(const std::vector<std::size_t> &indices,
+                                std::vector<FT> &dists) const {
+    for (std::size_t i = 0;i<indices.size();i++) {
+      const FT d = this->scalar_pdct(
+                                     this->constr_vec(m_point_on_primitive, this->point(indices[i])), 
+                                     m_normal);
+      dists[i] = d * d;
+    }
+  }
+
+  virtual void cos_to_normal(const std::vector<std::size_t> &indices, 
+                             std::vector<FT> &angles) const {
+    for (std::size_t i = 0;i<indices.size();i++) {
+      angles[i] = CGAL::abs(
+                            this->scalar_pdct(this->normal(indices[i]), m_normal));
+    }
+  }
+
+  FT cos_to_normal(const Point_3 &, const Vector_3 &n) const{
+    return CGAL::abs(this->scalar_pdct(n, m_normal));
+  } 
+    
+  virtual std::size_t minimum_sample_size() const {
+    return 3;
+  }
+
+  virtual bool supports_connected_component() const {
+    return true;
+  }
+
+private:
+  Point_3 m_point_on_primitive;
+  Vector_3 m_base1, m_base2, m_normal;
+  FT m_d;
+  /// \endcond
+};
+
+
+
+#endif // POINT_SET_CLASSIFICATION_ITEM_H
diff --git a/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item_config.h b/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item_config.h
new file mode 100644
index 00000000000..4e980c1e972
--- /dev/null
+++ b/Polyhedron/demo/Polyhedron/Scene_point_set_classification_item_config.h
@@ -0,0 +1,10 @@
+#ifndef SCENE_POINT_SET_CLASSIFICATION_ITEM_CONFIG_H
+#define SCENE_POINT_SET_CLASSIFICATION_ITEM_CONFIG_H
+
+#ifdef scene_point_set_classification_item_EXPORTS
+#  define SCENE_POINT_SET_CLASSIFICATION_ITEM_EXPORT Q_DECL_EXPORT
+#else
+#  define SCENE_POINT_SET_CLASSIFICATION_ITEM_EXPORT Q_DECL_IMPORT
+#endif
+
+#endif // SCENE_POINT_SET_CLASSIFICATION_ITEM_CONFIG_H
diff --git a/Stream_support/include/CGAL/IO/io.h b/Stream_support/include/CGAL/IO/io.h
index 81c68f61e8d..fd653223e88 100644
--- a/Stream_support/include/CGAL/IO/io.h
+++ b/Stream_support/include/CGAL/IO/io.h
@@ -177,6 +177,59 @@ public:
     return is; 
   }
 };
+
+template <>
+class Input_rep<float> {
+    float& t;
+public:
+  //! initialize with a reference to \a t.
+  Input_rep( float& tt) : t(tt) {}
+
+  std::istream& operator()( std::istream& is) const 
+  {
+    typedef std::istream istream;
+    typedef istream::char_type char_type;
+    typedef istream::int_type int_type;
+    typedef istream::traits_type traits_type;
+
+    std::string buffer;
+    buffer.reserve(32);
+
+    char_type c;
+    do {
+      const int_type i = is.get();
+      if(i == traits_type::eof()) {
+	return is;
+      }
+      c = static_cast<char_type>(i);
+    }while (std::isspace(c));
+    if(c == '-'){
+      buffer += '-';
+    } else if(c != '+'){
+      is.unget();
+    }
+    do {
+      const int_type i = is.get();
+      if(i == traits_type::eof()) {
+	is.clear(is.rdstate() & ~std::ios_base::failbit);
+	break;
+      }
+      c = static_cast<char_type>(i);
+      if(std::isdigit(c) || (c =='.') || (c =='E') || (c =='e') || (c =='+') || (c =='-')){
+        buffer += c;
+      }else{
+	is.unget();
+	break;
+      }
+    }while(true);
+
+    if(sscanf(buffer.c_str(), "%f", &t) != 1) {
+      // if a 'buffer' does not contain a double, set the fail bit.
+      is.setstate(std::ios_base::failbit);
+    }
+    return is; 
+  }
+};
 #endif
 
 /*! \relates Input_rep