基于曲率的关键点提取器 / Curvature-based keypoint extractor More...

#include <curvature_keypoints.hpp>

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

Public Types
using	base_type = base_keypoint_extractor_t< curvature_keypoint_extractor_t< DataType, KNN >, DataType, KNN >

using	data_type = typename base_type::data_type

using	knn_type = typename base_type::knn_type

using	point_cloud = typename base_type::point_cloud

using	point_cloud_ptr = typename base_type::point_cloud_ptr

using	indices_vector = typename base_type::indices_vector

Public Member Functions
	curvature_keypoint_extractor_t ()=default

std::size_t	set_input_impl (const point_cloud &cloud)
	CRTP实现方法 - 设置输入点云 / CRTP implementation - set input point cloud.

std::size_t	set_input_impl (const point_cloud_ptr &cloud)

std::size_t	set_knn_impl (const knn_type &knn)
	CRTP实现方法 - 设置KNN算法 / CRTP implementation - set KNN algorithm.

std::size_t	set_search_radius_impl (data_type radius)
	CRTP实现方法 - 设置搜索半径 / CRTP implementation - set search radius.

void	enable_parallel_impl (bool enable)
	CRTP实现方法 - 启用并行处理 / CRTP implementation - enable parallel processing.

indices_vector	extract_impl ()
	CRTP实现方法 - 提取关键点 / CRTP implementation - extract keypoints.

void	extract_impl (indices_vector &keypoint_indices)

point_cloud	extract_keypoints_impl ()

void	extract_keypoints_impl (point_cloud_ptr output)

void	set_curvature_threshold (data_type threshold)
	设置曲率阈值 / Set curvature threshold

void	set_min_neighbors (std::size_t min_neighbors)
	设置最小邻居数量 / Set minimum number of neighbors

void	set_non_maxima_radius (data_type radius)
	设置非极大值抑制半径 / Set non-maxima suppression radius

data_type	get_curvature_threshold () const
	获取曲率阈值 / Get curvature threshold

std::size_t	get_min_neighbors () const
	获取最小邻居数量 / Get minimum number of neighbors

data_type	get_non_maxima_radius () const
	获取非极大值抑制半径 / Get non-maxima suppression radius

Detailed Description

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

基于曲率的关键点提取器 / Curvature-based keypoint extractor

Template Parameters

DataType	数据类型（float或double） / Data type (float or double)
KNN	最近邻搜索算法类型，默认使用 kdtree_generic_t / K-nearest neighbor search algorithm type, defaults to kdtree_generic_t

该类通过计算每个点的主曲率来检测关键点。曲率较大的点通常对应于角点、边缘等几何特征 / This class detects keypoints by computing principal curvatures at each point. Points with high curvature typically correspond to corners, edges, and other geometric features

// 基本使用示例 / Basic usage example
using data_type = float;
point_cloud_t<data_type> cloud = load_point_cloud();
 
// 创建曲率关键点提取器 / Create curvature keypoint extractor
curvature_keypoint_extractor_t<data_type, kdtree_t<data_type>> extractor;
 
// 设置参数 / Set parameters
extractor.set_input(cloud);
extractor.set_search_radius(0.5f);
extractor.set_curvature_threshold(0.01f);
extractor.set_non_maxima_radius(0.3f);
extractor.set_min_neighbors(10);
 
// 设置KNN算法 / Set KNN algorithm
kdtree_t<data_type> kdtree;
extractor.set_knn(kdtree);
 
// 提取关键点 / Extract keypoints
auto keypoints = extractor.extract();
std::cout << "找到 " << keypoints.size() << " 个曲率关键点 / Found " << keypoints.size() << " curvature keypoints" << std::endl;

// 并行处理示例 / Parallel processing example
curvature_keypoint_extractor_t<float, bfknn_parallel_t<float>> parallel_extractor;
parallel_extractor.set_input(large_cloud);
parallel_extractor.enable_parallel(true);  // 启用多线程 / Enable multithreading
 
bfknn_parallel_t<float> parallel_knn;
parallel_extractor.set_knn(parallel_knn);
 
auto keypoints = parallel_extractor.extract();

Member Typedef Documentation

◆ base_type

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

using toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::base_type = base_keypoint_extractor_t<curvature_keypoint_extractor_t<DataType, KNN>, DataType, KNN>

◆ data_type

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

using toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::data_type = typename base_type::data_type

◆ indices_vector

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

using toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::indices_vector = typename base_type::indices_vector

◆ knn_type

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

using toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::knn_type = typename base_type::knn_type

◆ point_cloud

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

using toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::point_cloud = typename base_type::point_cloud

◆ point_cloud_ptr

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

using toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::point_cloud_ptr = typename base_type::point_cloud_ptr

Constructor & Destructor Documentation

◆ curvature_keypoint_extractor_t()

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

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

default

Member Function Documentation

◆ enable_parallel_impl()

template<typename DataType , typename KNN >

void toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::enable_parallel_impl ( bool enable )

CRTP实现方法 - 启用并行处理 / CRTP implementation - enable parallel processing.

◆ extract_impl() [1/2]

template<typename DataType , typename KNN >

curvature_keypoint_extractor_t< DataType, KNN >::indices_vector toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::extract_impl ( )

CRTP实现方法 - 提取关键点 / CRTP implementation - extract keypoints.

◆ extract_impl() [2/2]

template<typename DataType , typename KNN >

void toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::extract_impl ( indices_vector & keypoint_indices )

◆ extract_keypoints_impl() [1/2]

template<typename DataType , typename KNN >

curvature_keypoint_extractor_t< DataType, KNN >::point_cloud toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::extract_keypoints_impl ( )

◆ extract_keypoints_impl() [2/2]

template<typename DataType , typename KNN >

void toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::extract_keypoints_impl ( point_cloud_ptr output )

◆ get_curvature_threshold()

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

data_type toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::get_curvature_threshold ( ) const

inline

获取曲率阈值 / Get curvature threshold

Returns: 当前的曲率阈值 / Current curvature threshold

◆ get_min_neighbors()

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

std::size_t toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::get_min_neighbors ( ) const

inline

获取最小邻居数量 / Get minimum number of neighbors

Returns: 当前的最小邻居数量 / Current minimum number of neighbors

◆ get_non_maxima_radius()

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

data_type toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::get_non_maxima_radius ( ) const

inline

获取非极大值抑制半径 / Get non-maxima suppression radius

Returns: 当前的非极大值抑制半径 / Current non-maxima suppression radius

◆ set_curvature_threshold()

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

void toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::set_curvature_threshold ( data_type threshold )

inline

设置曲率阈值 / Set curvature threshold

Parameters

threshold 曲率阈值，超过此值的点被认为是关键点候选 / Curvature threshold, points exceeding this value are considered keypoint candidates

较小的阈值会检测更多关键点，较大的阈值只检测曲率显著的点 / Smaller thresholds detect more keypoints, larger thresholds only detect points with significant curvature

◆ set_input_impl() [1/2]

template<typename DataType , typename KNN >

std::size_t toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::set_input_impl ( const point_cloud & cloud )

CRTP实现方法 - 设置输入点云 / CRTP implementation - set input point cloud.

◆ set_input_impl() [2/2]

template<typename DataType , typename KNN >

std::size_t toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::set_input_impl ( const point_cloud_ptr & cloud )

◆ set_knn_impl()

template<typename DataType , typename KNN >

std::size_t toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::set_knn_impl ( const knn_type & knn )

CRTP实现方法 - 设置KNN算法 / CRTP implementation - set KNN algorithm.

◆ set_min_neighbors()

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

void toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::set_min_neighbors ( std::size_t min_neighbors )

inline

设置最小邻居数量 / Set minimum number of neighbors

Parameters

min_neighbors 计算曲率所需的最小邻居点数 / Minimum number of neighbor points required for curvature computation

较少的邻居可能导致曲率估计不稳定，建议至少使用10个邻居 / Fewer neighbors may lead to unstable curvature estimation, at least 10 neighbors are recommended

◆ set_non_maxima_radius()

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

void toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::set_non_maxima_radius ( data_type radius )

inline

设置非极大值抑制半径 / Set non-maxima suppression radius

Parameters

radius 非极大值抑制的半径 / Radius for non-maxima suppression

在此半径内只保留曲率最大的点作为关键点 / Only the point with maximum curvature within this radius is kept as keypoint

◆ set_search_radius_impl()

template<typename DataType , typename KNN >

std::size_t toolbox::pcl::curvature_keypoint_extractor_t< DataType, KNN >::set_search_radius_impl ( data_type radius )

CRTP实现方法 - 设置搜索半径 / CRTP implementation - set search radius.

The documentation for this class was generated from the following files:

src/include/cpp-toolbox/pcl/features/curvature_keypoints.hpp
src/include/cpp-toolbox/pcl/features/impl/curvature_keypoints_impl.hpp

Public Types

Public Member Functions

Detailed Description

Member Typedef Documentation

◆ base_type

◆ data_type

◆ indices_vector

◆ knn_type

◆ point_cloud

◆ point_cloud_ptr

Constructor & Destructor Documentation

◆ curvature_keypoint_extractor_t()

Member Function Documentation

◆ enable_parallel_impl()

◆ extract_impl() [1/2]

◆ extract_impl() [2/2]

◆ extract_keypoints_impl() [1/2]

◆ extract_keypoints_impl() [2/2]

◆ get_curvature_threshold()

◆ get_min_neighbors()

◆ get_non_maxima_radius()

◆ set_curvature_threshold()

◆ set_input_impl() [1/2]

◆ set_input_impl() [2/2]

◆ set_knn_impl()

◆ set_min_neighbors()

◆ set_non_maxima_radius()

◆ set_search_radius_impl()