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Surface reconstruction: a bit of cleanup of normal estimation + comment

This commit is contained in:
Pierre Alliez 2008-03-24 12:05:27 +00:00
parent c6307bb753
commit e02796f8ac
5 changed files with 24 additions and 16 deletions
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6
Surface_reconstruction_3/include/CGAL/estimate_normals_jet_fitting_3.h
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@ -30,7 +30,6 @@
CGAL_BEGIN_NAMESPACE
/// Estimate normal direction using linear least
/// squares fitting of a plane on the K nearest neighbors.
///
@ -47,7 +46,7 @@ estimate_normal_jet_fitting_3(const typename Kernel::Point_3& query, ///< 3D poi
const unsigned int K,
const unsigned int degre_fitting = 2)
{
// Basic geometric types
// basic geometric types
typedef typename Kernel::Point_3 Point;
typedef typename Kernel::Vector_3 Vector;
typedef OrientedNormal_3 Oriented_normal;
@ -109,7 +108,7 @@ estimate_normals_jet_fitting_3(InputIterator first, ///< input points
const unsigned int degre_fitting = 2)
{
// Hard-code the Normal type as back_insert_iterator value_type is wrong (VC++ 2003)
//typedef typename std::iterator_traits<OutputIterator>::value_type Normal;
// typedef typename std::iterator_traits<OutputIterator>::value_type Normal;
typedef CGAL::Oriented_normal_3<Kernel> Normal;
// types for K-nearest neighbor search structure
@ -158,7 +157,6 @@ estimate_normals_jet_fitting_3(InputIterator first, ///< input points
estimate_normals_jet_fitting_3(first,beyond,normals,K,Kernel(),degre_fitting);
}
CGAL_END_NAMESPACE
#endif // CGAL_ESTIMATE_NORMALS_JET_FITTING_3_H

5
Surface_reconstruction_3/include/CGAL/estimate_normals_pca_3.h
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@ -30,7 +30,6 @@
CGAL_BEGIN_NAMESPACE
/// Estimate normal direction using linear least
/// squares fitting of a plane on the K nearest neighbors.
///
@ -46,7 +45,7 @@ estimate_normal_pca_3(const typename Kernel::Point_3& query, ///< 3D point whose
Tree& tree, ///< KD-tree
const unsigned int K)
{
// Basic geometric types
// basic geometric types
typedef typename Kernel::Point_3 Point;
typedef typename Kernel::Plane_3 Plane;
typedef typename Kernel::Vector_3 Vector;
@ -102,7 +101,7 @@ estimate_normals_pca_3(InputIterator first, ///< input points
const Kernel& kernel)
{
// Hard-code the Normal type as back_insert_iterator value_type is wrong (VC++ 2003)
//typedef typename std::iterator_traits<OutputIterator>::value_type Normal;
// typedef typename std::iterator_traits<OutputIterator>::value_type Normal;
typedef CGAL::Oriented_normal_3<Kernel> Normal;
// types for K-nearest neighbor search structure

3
Surface_reconstruction_3/include/CGAL/estimate_normals_poles_3.h
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@ -31,6 +31,9 @@ CGAL_BEGIN_NAMESPACE
// triangulation, use the parameter otherwise (careful, 8 vertices
// are added in order to bound all Voronoi cells. The last parameter
// specifies if these points should be removed.
// Pierre: I would suggest not to use pointers - simply the user
// can call the function without any reference to a DT.
template < typename InputIterator,
typename OutputIterator,
typename Kernel,

2
Surface_reconstruction_3/include/CGAL/k_nearest_neighbor.h
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@ -89,7 +89,7 @@ struct Construct_coord_iterator {
// We have put the glue layer in this file as well, that is a class that
// allows to iterate over the Cartesian coordinates of the KVertex, and a
// class to construct such an iterator for a KVertex.
//We next need a distance class
// We next need a distance class
template <class Vertex_handle>
struct Distance {

24
Surface_reconstruction_3/test/Surface_reconstruction_3/normal_estimation_test.cpp
View File

@ -1,5 +1,3 @@
// poisson_reconstruction_test.cpp
#include <CGAL/basic.h> // include basic.h before testing #defines
// CGAL
@ -19,48 +17,59 @@
#include <iterator>
// types
typedef CGAL::Simple_cartesian<double> Kernel;
typedef CGAL::Simple_cartesian<float> Kernel;
typedef Kernel::FT FT;
typedef Kernel::Point_3 Point;
typedef Kernel::Vector_3 Vector;
typedef CGAL::Oriented_normal_3<Kernel> Normal;
// read point from .xyz file
// read point set from .xyz file
bool read_point_set(char *input_filename,
std::list<Point>& points)
{
std::cerr << " Open " << input_filename << " for reading...";
std::ifstream stream(input_filename);
if(!stream.is_open())
{
std::cerr << "failed" << std::endl;
return false;
}
// read point set
Point point;
while(!stream.fail())
{
stream >> point;
points.push_back(point);
}
std::cerr << "ok (" << points.size() << " points)" << std::endl;
return true;
}
void test_pca(std::list<Point>& points,
const unsigned int k)
{
std::cerr << " Estimate normals using KNN and point-based PCA...";
std::list<Normal> normals;
CGAL::estimate_normals_pca_3(points.begin(),points.end(),std::back_inserter(normals),k);
std::cerr << "ok" << std::endl;
}
void test_jet_fitting(std::list<Point>& points,
const unsigned int k)
{
std::cerr << " Estimate normals using KNN and jet fitting...";
std::list<Normal> normals;
CGAL::estimate_normals_jet_fitting_3(points.begin(),points.end(),std::back_inserter(normals),k);
std::cerr << "ok" << std::endl;
}
// main function
int main(int argc,
char * argv[])
{
std::cerr << "NORMAL ESTIMATION" << std::endl;
std::cerr << "Test the normal estimation methods" << std::endl;
std::cerr << "Normal estimation test" << std::endl;
if(argc < 2)
{
@ -77,10 +86,9 @@ int main(int argc,
{
test_pca(points,k);
test_jet_fitting(points,k);
std::cerr << "OK" << std::endl;
}
else
std::cerr << "Unable to open file " << argv[i] << std::endl;
std::cerr << " Unable to open file " << argv[i] << std::endl;
}
return EXIT_SUCCESS;
}