feature.h File Reference

#include <boost/function.hpp>
#include <boost/bind.hpp>
#include <boost/mpl/size.hpp>
#include "pcl/pcl_base.h"
#include "pcl/common/eigen.h"
#include "pcl/kdtree/tree_types.h"
#include "pcl/kdtree/kdtree.h"
#include "pcl/kdtree/kdtree_flann.h"
#include "pcl/kdtree/organized_data.h"
#include "pcl/io/io.h"
#include "pcl/features/feature.hpp"

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Classes
class	pcl::Feature< PointInT, PointOutT >
	Feature represents the base feature class. Some generic 3D operations that are applicable to all features are defined here as static methods. More...
class	pcl::FeatureFromNormals< PointInT, PointNT, PointOutT >
struct	pcl::NdCentroidFunctor< PointT >
	Helper functor structure for n-D centroid estimation. More...
Namespaces
namespace	pcl
Functions
template<typename PointT >
void	pcl::compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Vector4f &centroid)
	Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.
template<typename PointT >
void	pcl::compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Vector4f &centroid)
	Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.
template<typename PointT >
void	pcl::compute3DCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &centroid)
	Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
template<typename PointT >
void	pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
template<typename PointT >
void	pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
template<typename PointT >
void	pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
template<typename PointT >
void	pcl::computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices.
template<typename PointT >
void	pcl::computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices.
template<typename PointT >
void	pcl::computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute normalized the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of points in the point cloud.
template<typename PointT >
void	pcl::computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::VectorXf &centroid)
	General, all purpose nD centroid estimation for a set of points using their indices.
template<typename PointT >
void	pcl::computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::VectorXf &centroid)
	General, all purpose nD centroid estimation for a set of points using their indices.
template<typename PointT >
void	pcl::computeNDCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::VectorXf &centroid)
	General, all purpose nD centroid estimation for a set of points using their indices.
void	pcl::solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix, float &nx, float &ny, float &nz, float &curvature)
	Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.
void	pcl::solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix, const Eigen::Vector4f &point, Eigen::Vector4f &plane_parameters, float &curvature)
	Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.