PFH (Point Feature Histogram) descriptor extractor. More...

#include <pfh_extractor.hpp>

Inheritance diagram for toolbox::pcl::pfh_extractor_t< DataType, KNN >:

Public Types
using	base_type = base_descriptor_extractor_t< pfh_extractor_t< DataType, KNN >, DataType, pfh_signature_t< DataType > >

using	data_type = DataType

using	signature_type = pfh_signature_t< DataType >

using	knn_type = KNN

using	point_cloud = toolbox::types::point_cloud_t< DataType >

using	point_cloud_ptr = std::shared_ptr< point_cloud >

Public Member Functions
	pfh_extractor_t ()=default

std::size_t	set_input (const point_cloud &cloud)
	Set the input point cloud.

std::size_t	set_input (const point_cloud_ptr &cloud)

std::size_t	set_knn (const knn_type &knn)
	Set the KNN search algorithm.

std::size_t	set_search_radius (data_type radius)
	Set the search radius for neighbor search.

std::size_t	set_num_neighbors (std::size_t num_neighbors)
	Set the maximum number of neighbors to consider.

void	set_normals (const point_cloud_ptr &normals)
	Set the point cloud normals (optional, will be computed if not provided)

void	set_num_subdivisions (std::size_t subdivisions)
	Set the number of subdivision for each angular feature (default: 5)

void	enable_parallel_impl (bool enable)
	Enable or disable parallel processing.

void	compute_impl (const point_cloud &cloud, const std::vector< std::size_t > &keypoint_indices, std::vector< signature_type > &descriptors) const
	Compute descriptors for given keypoints.

void	compute_impl (const point_cloud &cloud, const std::vector< std::size_t > &keypoint_indices, std::unique_ptr< std::vector< signature_type > > &descriptors) const

Detailed Description

template<typename DataType, typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>
class toolbox::pcl::pfh_extractor_t< DataType, KNN >

PFH (Point Feature Histogram) descriptor extractor.

Template Parameters

DataType	The data type (float or double)
KNN	The K-nearest neighbor search algorithm type

PFH encodes the local geometry around a point by computing angular features between all pairs of points in the neighborhood. It captures detailed geometric information but is computationally expensive with O(k^2) complexity.

Reference: Rusu, R. B., Blodow, N., Marton, Z. C., & Beetz, M. (2008). Aligning point cloud views using persistent feature histograms.

// Basic usage example
using data_type = float;
point_cloud_t<data_type> cloud = load_point_cloud();
 
// Create PFH descriptor extractor
pfh_extractor_t<data_type, kdtree_t<data_type>> extractor;
 
// Set parameters
extractor.set_input(cloud);
extractor.set_search_radius(0.05f);  // Search radius for neighbors
extractor.set_num_neighbors(30);     // Maximum number of neighbors
 
// Set up KNN search
kdtree_t<data_type> kdtree;
extractor.set_knn(kdtree);
 
// Extract descriptors for keypoints
std::vector<std::size_t> keypoint_indices = {10, 20, 30};
std::vector<pfh_signature_t<data_type>> descriptors;
extractor.compute(cloud, keypoint_indices, descriptors);

Member Typedef Documentation

◆ base_type

template<typename DataType , typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>

using toolbox::pcl::pfh_extractor_t< DataType, KNN >::base_type = base_descriptor_extractor_t<pfh_extractor_t<DataType, KNN>, DataType, pfh_signature_t<DataType> >

◆ data_type

template<typename DataType , typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>

using toolbox::pcl::pfh_extractor_t< DataType, KNN >::data_type = DataType

◆ knn_type

template<typename DataType , typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>

using toolbox::pcl::pfh_extractor_t< DataType, KNN >::knn_type = KNN

◆ point_cloud

template<typename DataType , typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>

using toolbox::pcl::pfh_extractor_t< DataType, KNN >::point_cloud = toolbox::types::point_cloud_t<DataType>

◆ point_cloud_ptr

template<typename DataType , typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>

using toolbox::pcl::pfh_extractor_t< DataType, KNN >::point_cloud_ptr = std::shared_ptr<point_cloud>

◆ signature_type

template<typename DataType , typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>

using toolbox::pcl::pfh_extractor_t< DataType, KNN >::signature_type = pfh_signature_t<DataType>

Constructor & Destructor Documentation

◆ pfh_extractor_t()

template<typename DataType , typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>

toolbox::pcl::pfh_extractor_t< DataType, KNN >::pfh_extractor_t ( )

default