mirror of https://github.com/CGAL/cgal
244 lines
8.1 KiB
C++
244 lines
8.1 KiB
C++
// Copyright (c) 2007-09 INRIA Sophia-Antipolis (France).
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// All rights reserved.
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//
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// This file is part of CGAL (www.cgal.org); you may redistribute it under
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// the terms of the Q Public License version 1.0.
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// See the file LICENSE.QPL distributed with CGAL.
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//
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// Licensees holding a valid commercial license may use this file in
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// accordance with the commercial license agreement provided with the software.
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//
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// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
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// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
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//
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// $URL$
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// $Id$
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//
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// Author(s) : Laurent Saboret and Nader Salman and Pierre Alliez
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#ifndef CGAL_REMOVE_OUTLIERS_H
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#define CGAL_REMOVE_OUTLIERS_H
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#include <CGAL/Search_traits_3.h>
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#include <CGAL/Orthogonal_k_neighbor_search.h>
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#include <CGAL/property_map.h>
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#include <CGAL/point_set_processing_assertions.h>
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#include <iterator>
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#include <algorithm>
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#include <map>
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CGAL_BEGIN_NAMESPACE
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// ----------------------------------------------------------------------------
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// Private section
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// ----------------------------------------------------------------------------
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namespace internal {
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/// Utility function for remove_outliers():
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/// Computes average squared distance to the K nearest neighbors.
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///
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/// @commentheading Precondition: k >= 2.
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///
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/// @commentheading Template Parameters:
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/// @param Kernel Geometric traits class.
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/// @param Tree KD-tree.
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///
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/// @return computed distance.
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template < typename Kernel,
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typename Tree >
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typename Kernel::FT
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compute_avg_knn_sq_distance_3(
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const typename Kernel::Point_3& query, ///< 3D point to project
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Tree& tree, ///< KD-tree
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unsigned int k) ///< number of neighbors
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{
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// geometric types
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typedef typename Kernel::FT FT;
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typedef typename Kernel::Point_3 Point;
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// types for K nearest neighbors search
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typedef typename CGAL::Search_traits_3<Kernel> Tree_traits;
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typedef typename CGAL::Orthogonal_k_neighbor_search<Tree_traits> Neighbor_search;
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typedef typename Neighbor_search::iterator Search_iterator;
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// Gather set of (k+1) neighboring points.
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// Perform k+1 queries (if in point set, the query point is
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// output first). Search may be aborted if k is greater
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// than number of input points.
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std::vector<Point> points; points.reserve(k+1);
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Neighbor_search search(tree,query,k+1);
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Search_iterator search_iterator = search.begin();
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unsigned int i;
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for(i=0;i<(k+1);i++)
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{
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if(search_iterator == search.end())
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break; // premature ending
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points.push_back(search_iterator->first);
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search_iterator++;
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}
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CGAL_point_set_processing_precondition(points.size() >= 1);
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// compute average squared distance
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typename Kernel::Compute_squared_distance_3 sqd;
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FT sq_distance = (FT)0.0;
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for(typename std::vector<Point>::iterator neighbor = points.begin(); neighbor != points.end(); neighbor++)
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sq_distance += sqd(*neighbor, query);
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sq_distance /= FT(points.size());
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return sq_distance;
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}
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} /* namespace internal */
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// ----------------------------------------------------------------------------
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// Public section
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// ----------------------------------------------------------------------------
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/// Removes outliers:
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/// - computes average squared distance to the K nearest neighbors,
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/// - and sorts the points in increasing order of average distance.
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///
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/// This method modifies the order of input points so as to pack all remaining points first,
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/// and returns an iterator over the first point to remove (see erase-remove idiom).
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/// For this reason it should not be called on sorted containers.
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///
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/// @commentheading Precondition: k >= 2.
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///
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/// @commentheading Template Parameters:
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/// @param InputIterator iterator over input points.
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/// @param PointPMap is a model of boost::ReadablePropertyMap with a value_type = Point_3<Kernel>.
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/// It can be omitted if InputIterator value_type is convertible to Point_3<Kernel>.
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/// @param Kernel Geometric traits class.
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/// It can be omitted and deduced automatically from PointPMap value_type.
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///
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/// @return iterator over the first point to remove.
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// This variant requires all parameters.
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template <typename InputIterator,
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typename PointPMap,
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typename Kernel
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>
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InputIterator
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remove_outliers(
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InputIterator first, ///< iterator over the first input point.
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InputIterator beyond, ///< past-the-end iterator over the input points.
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PointPMap point_pmap, ///< property map InputIterator -> Point_3
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unsigned int k, ///< number of neighbors.
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double threshold_percent, ///< percentage of points to remove.
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const Kernel& kernel) ///< geometric traits.
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{
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// geometric types
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typedef typename Kernel::FT FT;
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// basic geometric types
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typedef typename Kernel::Point_3 Point;
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// actual type of input points
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typedef typename std::iterator_traits<InputIterator>::value_type Enriched_point;
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// types for K nearest neighbors search structure
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typedef typename CGAL::Search_traits_3<Kernel> Tree_traits;
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typedef typename CGAL::Orthogonal_k_neighbor_search<Tree_traits> Neighbor_search;
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typedef typename Neighbor_search::Tree Tree;
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typedef typename Neighbor_search::iterator Search_iterator;
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// precondition: at least one element in the container.
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// to fix: should have at least three distinct points
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// but this is costly to check
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CGAL_point_set_processing_precondition(first != beyond);
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// precondition: at least 2 nearest neighbors
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CGAL_point_set_processing_precondition(k >= 2);
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CGAL_point_set_processing_precondition(threshold_percent >= 0 && threshold_percent <= 100);
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InputIterator it;
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// Instanciate a KD-tree search.
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// Note: We have to convert each input iterator to Point_3.
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std::vector<Point> kd_tree_points;
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for(it = first; it != beyond; it++)
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{
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Point point = get(point_pmap, it);
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kd_tree_points.push_back(point);
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}
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Tree tree(kd_tree_points.begin(), kd_tree_points.end());
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// iterate over input points and add them to multimap sorted by distance to k
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std::multimap<FT,Enriched_point> sorted_points;
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for(it = first; it != beyond; it++)
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{
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FT sq_distance = internal::compute_avg_knn_sq_distance_3<Kernel>(get(point_pmap,it), tree, k);
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sorted_points.insert( std::make_pair(sq_distance, *it) );
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}
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// Replaces [first, beyond) range by the multimap content.
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// Returns the iterator after the (100-threshold_percent) % best points.
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InputIterator first_point_to_remove = beyond;
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InputIterator dst = first;
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int first_index_to_remove = int(double(sorted_points.size()) * ((100.0-threshold_percent)/100.0));
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typename std::multimap<FT,Enriched_point>::iterator src;
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int index;
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for (src = sorted_points.begin(), index = 0;
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src != sorted_points.end();
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++src, ++index)
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{
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*dst++ = src->second;
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if (index == first_index_to_remove)
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first_point_to_remove = dst;
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}
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return first_point_to_remove;
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}
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/// @cond SKIP_IN_MANUAL
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// This variant deduces the kernel from the iterator type.
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template <typename InputIterator,
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typename PointPMap
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>
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InputIterator
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remove_outliers(
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InputIterator first, ///< iterator over the first input point
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InputIterator beyond, ///< past-the-end iterator
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PointPMap point_pmap, ///< property map InputIterator -> Point_3
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unsigned int k, ///< number of neighbors.
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double threshold_percent) ///< percentage of points to remove
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{
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typedef typename boost::property_traits<PointPMap>::value_type Point;
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typedef typename Kernel_traits<Point>::Kernel Kernel;
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return remove_outliers(
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first,beyond,
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point_pmap,
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k,threshold_percent,
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Kernel());
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}
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/// @endcond
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/// @cond SKIP_IN_MANUAL
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// This variant creates a default point property map = Dereference_property_map.
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template <typename InputIterator
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>
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InputIterator
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remove_outliers(
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InputIterator first, ///< iterator over the first input point
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InputIterator beyond, ///< past-the-end iterator
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unsigned int k, ///< number of neighbors.
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double threshold_percent) ///< percentage of points to remove
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{
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return remove_outliers(
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first,beyond,
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make_dereference_property_map(first),
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k,threshold_percent);
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}
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/// @endcond
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CGAL_END_NAMESPACE
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#endif // CGAL_REMOVE_OUTLIERS_H
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