Pierre Alliez, Sylvain Pion and Ankit Gupta
This package provides functions to analyze sets of objects in 2D and 3D. It provides the computation of axisaligned bounding boxes, centers of mass and principal component analysis for all bounded objects, as well as barycenters for sets of weighted points. Note that unlike most of the Cgal packages, this package uses approximation methods (in particular for linear least squares fitting) and is not intended to provide an exact canonical result in any sense.
A bounding box for a set of objects is a cuboid that contains the set. An axisaligned bounding box captures the maximum extents of all objects from the set within their coordinate system, i.e., a bounding box aligned with the axes of the coordinate system. Axisaligned bounding boxes are frequently used in geometric algorithms as an indication of the general location of a data set, for either display, firstapproximation spatial query, or spatial indexing purposes.
A centroid of a set of objects is their center of mass, i.e., the point whose coordinates are computed by means of coordinates of all points composing the objects. Note that although the general definition of center of mass incorporates a density function (and hence weighted means), the current implementation assumes a uniform density (see barycenter below defined for weighted points). For a point set {X_{1},X_{2},...,X_{N}} the centroid X is computed as
X = 

 . 
X = 

 , 
X = 

 , 
A barycenter of a set of weighted points is the point whose coordinates are computed by means of weighted coordinates of all weighted points from the set. When all weights are equal the barycenter coincides with the centroid.
Given a set of objects, linear least squares fitting amounts to finding the linear subspace which minimizes the sum of squared distances from all points composing the objects of the set, to their projection onto this linear subspace. Such linear subspace is obtained by socalled principal component analysis (PCA). PCA is defined as a transformation that transforms the objects to a new coordinate system such that the greatest variance by orthogonal projection of the objects comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. PCA is often used in geometric applications to reduce data sets to lower dimensions for analysis or approximation. Figure below illustrates (least squares) fitting of a line to a 2D point set, fitting of a line and a plane to a 3D point set and fitting of a plane to a set of 3D triangles.
Figure 68.1: Left: fitting a line to a 2D point set (centroid dotted in red). Middle: fitting a line and a plane to a 3D point set. Right: fitting a plane to a set of 3D triangles.
File: examples/Principal_component_analysis/bounding_box.cpp
#include <CGAL/Cartesian.h> #include <CGAL/bounding_box.h> #include <list> #include <iostream> typedef double FT; typedef CGAL::Cartesian<FT> K; typedef K::Point_2 Point_2; typedef K::Point_3 Point_3; int main() { // axisaligned bounding box of 2D points std::list<Point_2> points_2; points_2.push_back(Point_2(1.0, 0.0)); points_2.push_back(Point_2(2.0, 2.0)); points_2.push_back(Point_2(3.0, 5.0)); K::Iso_rectangle_2 c2 = CGAL::bounding_box(points_2.begin(), points_2.end()); std::cout << c2 << std::endl; // axisaligned bounding box of 3D points std::list<Point_3> points_3; points_3.push_back(Point_3(1.0, 0.0, 0.5)); points_3.push_back(Point_3(2.0, 2.0, 1.2)); points_3.push_back(Point_3(3.0, 5.0, 4.5)); K::Iso_cuboid_3 c3 = CGAL::bounding_box(points_3.begin(), points_3.end()); std::cout << c3 << std::endl; return 0; }
File: examples/Principal_component_analysis/centroid.cpp
#include <CGAL/Cartesian.h> #include <CGAL/centroid.h> #include <list> #include <iostream> typedef double FT; typedef CGAL::Cartesian<FT> K; typedef K::Point_2 Point_2; typedef K::Point_3 Point_3; typedef K::Triangle_3 Triangle_3; int main() { // centroid of 2D points std::list<Point_2> points_2; points_2.push_back(Point_2(1.0, 0.0)); points_2.push_back(Point_2(2.0, 2.0)); points_2.push_back(Point_2(3.0, 5.0)); Point_2 c2 = CGAL::centroid(points_2.begin(), points_2.end(),CGAL::Dimension_tag<0>()); std::cout << c2 << std::endl; // centroid of 3D points std::list<Point_3> points_3; points_3.push_back(Point_3(1.0, 0.0, 0.5)); points_3.push_back(Point_3(2.0, 2.0, 1.2)); points_3.push_back(Point_3(3.0, 5.0, 4.5)); Point_3 c3 = CGAL::centroid(points_3.begin(), points_3.end(),CGAL::Dimension_tag<0>()); std::cout << c3 << std::endl; // centroid of 3D triangles std::list<Triangle_3> triangles_3; Point_3 p(1.0, 0.0, 0.0); Point_3 q(1.0, 2.0, 0.0); Point_3 r(0.0, 1.0, 3.0); Point_3 s(0.0, 2.0, 5.0); triangles_3.push_back(Triangle_3(p,q,r)); triangles_3.push_back(Triangle_3(p,q,s)); c3 = CGAL::centroid(triangles_3.begin(), triangles_3.end(),CGAL::Dimension_tag<2>()); std::cout << c3 << std::endl; return 0; }
File: examples/Principal_component_analysis/barycenter.cpp
#include <CGAL/Cartesian.h> #include <CGAL/barycenter.h> #include <list> #include <iostream> #include <utility> typedef double FT; typedef CGAL::Cartesian<FT> K; typedef K::Point_2 Point_2; typedef K::Point_3 Point_3; int main() { // barycenter of 2D weighted points std::list<std::pair<Point_2, FT> > points_2; points_2.push_back(std::make_pair(Point_2(1.0, 0.0), 1.0)); points_2.push_back(std::make_pair(Point_2(2.0, 2.0), 2.0)); points_2.push_back(std::make_pair(Point_2(3.0, 5.0), 2.0)); Point_2 c2 = CGAL::barycenter(points_2.begin(), points_2.end()); std::cout << c2 << std::endl; // barycenter of 3D weighted points std::list<std::pair<Point_3, FT> > points_3; points_3.push_back(std::make_pair(Point_3(1.0, 0.0, 0.5), 1.0)); points_3.push_back(std::make_pair(Point_3(2.0, 2.0, 1.2), 2.0)); points_3.push_back(std::make_pair(Point_3(3.0, 5.0, 4.5), 5.0)); Point_3 c3 = CGAL::barycenter(points_3.begin(), points_3.end()); std::cout << c3 << std::endl; return 0; }
File: examples/Principal_component_analysis/linear_least_squares_fitting_points_2.cpp
#include <CGAL/Cartesian.h> #include <CGAL/linear_least_squares_fitting_2.h> #include <list> typedef double FT; typedef CGAL::Cartesian<FT> K; typedef K::Line_2 Line; typedef K::Point_2 Point; int main() { std::list<Point> points; points.push_back(Point(1.0,2.0)); points.push_back(Point(3.0,4.0)); points.push_back(Point(5.0,6.0)); // fit line Line line; linear_least_squares_fitting_2(points.begin(),points.end(),line,CGAL::Dimension_tag<0>()); return 0; }
File: examples/Principal_component_analysis/linear_least_squares_fitting_triangles_3.cpp
#include <CGAL/Cartesian.h> #include <CGAL/linear_least_squares_fitting_3.h> #include <list> typedef double FT; typedef CGAL::Cartesian<FT> K; typedef K::Line_3 Line; typedef K::Plane_3 Plane; typedef K::Point_3 Point; typedef K::Triangle_3 Triangle; int main(void) { std::list<Triangle> triangles; Point a(1.0,2.0,3.0); Point b(4.0,0.0,6.0); Point c(7.0,8.0,9.0); Point d(8.0,7.0,6.0); Point e(5.0,3.0,4.0); triangles.push_back(Triangle(a,b,c)); triangles.push_back(Triangle(a,b,d)); triangles.push_back(Triangle(d,e,c)); Line line; Plane plane; // fit plane to whole triangles linear_least_squares_fitting_3(triangles.begin(),triangles.end(),plane,CGAL::Dimension_tag<2>()); // fit line to triangle vertices linear_least_squares_fitting_3(triangles.begin(),triangles.end(),line, CGAL::Dimension_tag<0>()); return 0; }