diff --git a/Property_map/doc/Property_map/Doxyfile.in b/Property_map/doc/Property_map/Doxyfile.in
index 2167dbca4fe..a079ae055f4 100644
--- a/Property_map/doc/Property_map/Doxyfile.in
+++ b/Property_map/doc/Property_map/Doxyfile.in
@@ -3,4 +3,4 @@
 PROJECT_NAME = "CGAL ${CGAL_DOC_VERSION} - CGAL and Boost Property Maps"
 INPUT += ${CGAL_PACKAGE_INCLUDE_DIR}/CGAL/property_map.h
 EXCLUDE += ${CGAL_PACKAGE_INCLUDE_DIR}/CGAL/Dynamic_property_map.h
-EXAMPLE_PATH = ${CGAL_Point_set_processing_3_EXAMPLE_DIR}
+EXAMPLE_PATH += ${CGAL_Point_set_processing_3_EXAMPLE_DIR}
diff --git a/Property_map/doc/Property_map/Property_map.txt b/Property_map/doc/Property_map/Property_map.txt
index fef8999a0c6..d2ecf66d1df 100644
--- a/Property_map/doc/Property_map/Property_map.txt
+++ b/Property_map/doc/Property_map/Property_map.txt
@@ -6,6 +6,8 @@ namespace CGAL {
 
 \anchor chapterProperty_map
 
+\cgalAutoToc
+
 \authors Andreas Fabri and Laurent Saboret
 
 \section Property_mapA A Short Introduction to the Boost Property Maps Library
@@ -55,6 +57,20 @@ The following example reads a point set from an input file and writes it to a fi
 The following example reads a point set in the `xyz` format and computes the average spacing. %Index, position and color are stored in a tuple and accessed through property maps.
 \cgalExample{Point_set_processing_3/average_spacing_example.cpp}
 
+\section Property_mapCustom Writing Custom Property Maps
+
+Property maps are especially useful when using predefined data
+structures that are not part of the \cgal library: algorithms written
+with property maps can be called on these data structures provided the
+user writes the required property maps, without the need to create
+deep copies of potentially large data into \cgal formats.
+
+The following example shows how to write a readable point map and a
+read-write normal map to run \cgal normal estimation and orientation
+algorithm on raw `double` arrays:
+\cgalExample{Property_map/custom_property_map.cpp}
+
+
 */
 } /* namespace CGAL */
 
diff --git a/Property_map/doc/Property_map/examples.txt b/Property_map/doc/Property_map/examples.txt
index fe5b9e2f71c..30870650f01 100644
--- a/Property_map/doc/Property_map/examples.txt
+++ b/Property_map/doc/Property_map/examples.txt
@@ -2,4 +2,5 @@
 \example Point_set_processing_3/remove_outliers_example.cpp
 \example Point_set_processing_3/read_write_xyz_point_set_example.cpp
 \example Point_set_processing_3/average_spacing_example.cpp
+\example Property_map/custom_property_map.cpp
 */
diff --git a/Property_map/examples/Property_map/CMakeLists.txt b/Property_map/examples/Property_map/CMakeLists.txt
index 9b183930b64..d5717d3f5d2 100644
--- a/Property_map/examples/Property_map/CMakeLists.txt
+++ b/Property_map/examples/Property_map/CMakeLists.txt
@@ -32,4 +32,10 @@ endif()
 
 create_single_source_cgal_program( "dynamic_properties.cpp" )
 
+find_package(Eigen3 3.1.0) #(requires 3.1.0 or greater)
+if (EIGEN3_FOUND)
+  create_single_source_cgal_program( "custom_property_map.cpp" )
+  CGAL_target_use_Eigen(custom_property_map)
+endif()
+
 
diff --git a/Property_map/examples/Property_map/custom_property_map.cpp b/Property_map/examples/Property_map/custom_property_map.cpp
new file mode 100644
index 00000000000..540bec3cd2d
--- /dev/null
+++ b/Property_map/examples/Property_map/custom_property_map.cpp
@@ -0,0 +1,112 @@
+#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
+#include <CGAL/point_generators_3.h>
+#include <CGAL/jet_estimate_normals.h>
+#include <CGAL/mst_orient_normals.h>
+
+using Kernel = CGAL::Exact_predicates_inexact_constructions_kernel;
+using Point_3 = Kernel::Point_3;
+using Vector_3 = Kernel::Vector_3;
+using Generator = CGAL::Random_points_on_sphere_3<Point_3>;
+
+// Example of readable property map to get CGAL::Point_3 objects from
+// 3 coordinate arrays
+struct Custom_point_map
+{
+  using key_type = std::size_t; // The iterator's value type is an index
+  using value_type = Point_3;   // The object manipulated by the algorithm is a Point_3
+  using reference = Point_3;    // The object does not exist in memory, so there's no reference
+  using category = boost::readable_property_map_tag; // The property map is only used for reading
+
+  double *x, *y, *z;
+
+  Custom_point_map (double* x = nullptr, double* y = nullptr, double* z = nullptr)
+    : x(x), y(y), z(z) { }
+
+  // The get() function returns the object expected by the algorithm (here, Point_3)
+  friend Point_3 get (const Custom_point_map& map, std::size_t idx)
+  {
+    return Point_3 (map.x[idx], map.y[idx], map.z[idx]);
+  }
+};
+
+// Example of read-write property map to get CGAL::Vector_3 objects from
+// a buffer array and put CGAL::Vector_3 values in this buffer
+struct Custom_normal_map
+{
+  using key_type = std::size_t; // The iterator's value type is an index
+  using value_type = Vector_3;  // The object manipulated by the algorithm is a Vector_3
+  using reference = Vector_3;   // The object does not exist in memory, so there's no reference
+  using category = boost::read_write_property_map_tag; // The property map is used both
+                                                       // for reading and writing data
+  double *buffer;
+
+  Custom_normal_map (double* buffer = nullptr)
+    : buffer (buffer) { }
+
+  // The get() function returns the object expected by the algorithm (here, Vector_3)
+  friend Vector_3 get (const Custom_normal_map& map, std::size_t idx)
+  {
+    return Vector_3 (map.buffer[idx * 3    ],
+                     map.buffer[idx * 3 + 1],
+                     map.buffer[idx * 3 + 2]);
+  }
+
+  // The put() function updated the user's data structure from the
+  // object handled by the algorithm (here Vector_3)
+  friend void put (const Custom_normal_map& map, std::size_t idx, const Vector_3& vector_3)
+  {
+    map.buffer[idx * 3    ] = vector_3.x();
+    map.buffer[idx * 3 + 1] = vector_3.y();
+    map.buffer[idx * 3 + 2] = vector_3.z();
+  }
+};
+
+
+int main()
+{
+  constexpr std::size_t nb_points = 1000;
+
+  // in this example, points are stored as separate coordinate arrays
+  double x[nb_points];
+  double y[nb_points];
+  double z[nb_points];
+
+  // generate random points
+  Generator generator;
+  for (std::size_t i = 0; i < nb_points; ++ i)
+  {
+    Point_3 p = *(generator ++ );
+    x[i] = p.x();
+    y[i] = p.y();
+    z[i] = p.z();
+  }
+
+  // normals are stored as a contiguous double array
+  double normals[3 *nb_points];
+
+  // we use a vector of indices to access arrays
+  std::vector<std::size_t> indices;
+  indices.reserve (nb_points);
+  for (std::size_t i = 0; i < nb_points; ++ i)
+    indices.push_back(i);
+
+  // estimate and orient normals using directly user's data structure
+  // instead of creating deep copies using Point_3 and Vector_3
+  CGAL::jet_estimate_normals<CGAL::Sequential_tag>
+    (indices, 12,
+     CGAL::parameters::point_map (Custom_point_map(x,y,z)).
+     normal_map (Custom_normal_map(normals)));
+
+  CGAL::mst_orient_normals
+    (indices, 12,
+     CGAL::parameters::point_map (Custom_point_map(x,y,z)).
+     normal_map (Custom_normal_map(normals)));
+
+  // Display first 10 points+normals
+  for (std::size_t i = 0; i < 10; ++ i)
+    std::cerr << "Point(" << i << ") = " << x[i] << " " << y[i] << " " << z[i]
+              << "\tNormal(" << i << ") = "
+              << normals[3*i] << " " << normals[3*i+1] << " " << normals[3*i+2] << std::endl;
+
+  return EXIT_SUCCESS;
+}