基于PCA的法向量提取器 / PCA-based normal extractor More...

#include <pca_norm.hpp>

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

Public Types
using	base_type = base_norm_extractor_t< pca_norm_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

Public Member Functions
std::size_t	set_input_impl (const point_cloud &cloud)
	设置输入点云的实现 / Implementation of setting input point cloud

std::size_t	set_input_impl (const point_cloud_ptr &cloud)
	设置输入点云的实现（智能指针版本） / Implementation of setting input point cloud (smart pointer version)

std::size_t	set_knn_impl (const knn_type &knn)
	设置KNN搜索算法的实现 / Implementation of setting KNN search algorithm

std::size_t	set_num_neighbors_impl (std::size_t num_neighbors)
	设置近邻数量的实现 / Implementation of setting number of neighbors

point_cloud	extract_impl ()
	提取法向量的实现 / Implementation of extracting normals

void	extract_impl (point_cloud_ptr output)
	提取法向量到指定输出的实现 / Implementation of extracting normals to specified output

void	enable_parallel (bool enable)
	启用或禁用并行计算 / Enable or disable parallel computation

Detailed Description

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

基于PCA的法向量提取器 / PCA-based normal extractor

该类使用主成分分析（PCA）来估计点云中每个点的法向量。通过分析每个点的邻域，计算协方差矩阵的特征向量，最小特征值对应的特征向量即为法向量方向。 This class uses Principal Component Analysis (PCA) to estimate normals for each point in the point cloud. By analyzing the neighborhood of each point, it computes the eigenvectors of the covariance matrix, where the eigenvector corresponding to the smallest eigenvalue represents the normal direction.

Template Parameters

DataType	数据类型（如float或double） / Data type (e.g., float or double)
KNN	KNN搜索算法类型，默认为kdtree_generic_t / KNN search algorithm type, default is kdtree_generic_t

// 基本使用示例 / Basic usage example
pca_norm_extractor_t<float> norm_extractor;
point_cloud_t<float> cloud = load_point_cloud("data.pcd");
 
// 设置输入和参数 / Set input and parameters
norm_extractor.set_input(cloud);
norm_extractor.set_num_neighbors(30);  // 使用30个近邻点 / Use 30 neighbors
 
// 提取法向量 / Extract normals
point_cloud_t<float> normals = norm_extractor.extract();

// 使用并行计算加速 / Using parallel computation for acceleration
pca_norm_extractor_t<float> norm_extractor;
norm_extractor.enable_parallel(true);  // 启用并行 / Enable parallel
norm_extractor.set_input(large_cloud);
norm_extractor.set_num_neighbors(50);
 
// 使用自定义KNN算法 / Using custom KNN algorithm
bfknn_parallel_t<float> custom_knn;
norm_extractor.set_knn(custom_knn);
 
auto normals = norm_extractor.extract();