\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.13.1 - 3D Point Set
Point_set_3/point_set_algo.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Point_set_3.h>
#include <CGAL/jet_estimate_normals.h>
#include <CGAL/grid_simplify_point_set.h>
#include <CGAL/point_generators_3.h>
#include <fstream>
#include <limits>
typedef Kernel::FT FT;
typedef Kernel::Point_3 Point;
typedef Kernel::Vector_3 Vector;
typedef CGAL::Random_points_on_sphere_3<Point> Point_generator;
typedef CGAL::Point_set_3<Point> Point_set;
<Kernel, Point_set, Point_set::Point_map, Point_set::Vector_map> Traits;
int main (int, char**)
{
Point_set point_set;
// Generate points on a unit sphere
Point_generator generator(1.);
std::size_t nb_pts = 10000;
point_set.reserve (nb_pts);
for (std::size_t i = 0; i < nb_pts; ++ i)
point_set.insert (*(generator ++));
// Add normal property and estimate normal values
point_set.add_normal_map();
CGAL::jet_estimate_normals<CGAL::Sequential_tag>
(point_set,
12, // Number of neighbors
point_set.parameters(). // Named parameters provided by Point_set_3
degree_fitting(2)); // additional named parameter specific to jet_estimate_normals
// Simplify point set
(point_set,
0.1); // Size of grid cell
// point_set.parameters() can be omitted if no additional named parameter is needed
std::vector<std::string> properties = point_set.properties();
std::cerr << "Properties:" << std::endl;
for (std::size_t i = 0; i < properties.size(); ++ i)
std::cerr << " * " << properties[i] << std::endl;
// Detect sphere with RANSAC
Efficient_ransac ransac;
ransac.set_input(point_set,
point_set.point_map(), // Call built-in property map
point_set.normal_map()); // Call built-in property map
ransac.add_shape_factory<Sphere>();
Efficient_ransac::Parameters parameters;
parameters.probability = 0.05;
parameters.min_points = std::size_t(point_set.size() / 3);
parameters.epsilon = 0.01;
parameters.cluster_epsilon = 0.5;
parameters.normal_threshold = 0.9;
ransac.detect(parameters);
BOOST_FOREACH(boost::shared_ptr<Efficient_ransac::Shape> shape, ransac.shapes())
if (Sphere* sphere = dynamic_cast<Sphere*>(shape.get()))
std::cerr << "Detected sphere of center " << sphere->center() // Center should be approx 0, 0, 0
<< " and of radius " << sphere->radius() << std::endl; // Radius should be approx 1
return 0;
}