首先PCL定义了搜索的基类pcl::search::Search<PointInT>
template<typename PointT>
class Search其子类包括：KD树，八叉树，FLANN快速搜索，暴力搜索（brute force），有序点云搜索。
| The pcl_search library provides methods for searching for nearest neighbors using different data structures, including:
|
search类都定义了两种最常见的近邻搜索模式：k近邻搜索，球状固定距离半径近邻搜索。
virtual int nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const = 0;
virtual int radiusSearch (const PointT& point, double radius, std::vector<int>& k_indices, std::vector<float>& k_sqr_distances, unsigned int max_nn = 0) const = 0;还有一种比较有用的方式是：圆柱固定距离半径搜索，即在XOY平面的投影距离，目前没有看到PCL中的专门的实现方式，可以通过一种折中的方法解决octree。
还有就是通过累积地图实现的投影到XOY平面内的简单搜索。
Weinmann, M., et al. (2015). "Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers." ISPRS Journal of Photogrammetry and Remote Sensing 105: 286-304.
在feature模块中大量使用了近邻搜索的东西。近邻搜索是很多点云计算的基础功能。
示例
如下调用了点云KD树近邻搜索实现了8个基于特征值的点云特征计算：
1 QString filePly = dlg.txtPath->text();
2 std::wstring pszRoomFile1 = filePly.toStdWString();
3 char buffer[256];
4 size_t ret = wcstombs(buffer, pszRoomFile1.c_str(), sizeof(buffer));
5 const char * pszShapeFile = buffer;
6 char * file_name = (char*)pszShapeFile;
7 pcl::PointCloud<pcl::PointXYZ>::Ptr input_(new pcl::PointCloud<pcl::PointXYZ>);
8 /*------------读取PLY文件-------------*/
9 if (pcl::io::loadPLYFile<pcl::PointXYZ>(file_name, *input_) == -1) //* load the file
10 {
11 PCL_ERROR("Couldn't read file test_pcd.pcd \n");
12 QMessageBox::information(NULL, "Title", "Content", QMessageBox::Yes | QMessageBox::No, QMessageBox::Yes);
13 }
14 double search_radius_ = dlg.sb_scale2->value();
15 int k_=10;
16 double search_parameter_ = 0.0;
17 /*------------构造索引-------------*/
18 boost::shared_ptr <std::vector<int> > indices_;
19 if (!indices_)
20 {
21 indices_.reset(new std::vector<int>);
22 try
23 {
24 indices_->resize(input_->points.size());
25 }
26 catch (const std::bad_alloc&)
27 {
28 PCL_ERROR("[initCompute] Failed to allocate %lu indices.\n", input_->points.size());
29 }
30 for (size_t i = 0; i < indices_->size(); ++i) { (*indices_)[i] = static_cast<int>(i); }
31 }
32 std::vector<int> nn_indices(k_);
33 std::vector<float> nn_dists(k_);
34 /*---------------构造KD树-------------*/
35 //pcl::search::Search<pcl::PointXYZ>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZ> >(new pcl::search::KdTree<pcl::PointXYZ>);
36 pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
37 tree->setInputCloud(input_);
38 SearchMethodSurface search_method_surface_;
39 if (search_radius_ != 0.0)
40 {
41 search_parameter_ = search_radius_;
42 int (pcl::search::Search<pcl::PointXYZ>::*radiusSearchSurface)(const PointCloudIn &cloud, int index, double radius,
43 std::vector<int> &k_indices, std::vector<float> &k_distances,
44 unsigned int max_nn) const = &pcl::search::Search<pcl::PointXYZ>::radiusSearch;
45 search_method_surface_ = boost::bind(radiusSearchSurface, boost::ref(tree), _1, _2, _3, _4, _5, 0);
46 }
47 else
48 {
49 search_parameter_ = k_;
50 int (pcl::search::Search<pcl::PointXYZ>::*nearestKSearchSurface)(const PointCloudIn &cloud, int index, int k, std::vector<int> &k_indices,
51 std::vector<float> &k_distances) const = &pcl::search::Search<pcl::PointXYZ>::nearestKSearch;
52 search_method_surface_ = boost::bind(nearestKSearchSurface, boost::ref(tree), _1, _2, _3, _4, _5);
53 }
54 pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>);
55 PointCloudOut& output = *cloud_normals;
56 AxPointCloudOut output_pts;
57 if (input_->size() < 3)
58 {
59 return;
60 }
61 else/*--------------计算特征值和特征向量-------------*/
62 {
63 output_pts.resize(input_->size());
64 //output_pts.reserve(input_->size(), (new AxPoint()));
65 #ifdef _OPENMP
66 #pragma omp parallel for shared (output_pts) private (nn_indices, nn_dists) num_threads(threads_)
67 #endif
68 // Iterating over the entire index vector
69 for (int idx = 0; idx < static_cast<int> (indices_->size()); ++idx)
70 {
71 if (search_method_surface_(*input_, (*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
72 {
73 output.points[idx].normal[0] = output.points[idx].normal[1] = output.points[idx].normal[2] = output.points[idx].curvature = std::numeric_limits<float>::quiet_NaN();
74
75 output.is_dense = false;
76 continue;
77 }
78
79 EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
80 // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
81 Eigen::Vector4f xyz_centroid;
82
83 if (pcl::computeMeanAndCovarianceMatrix(*input_, nn_indices, covariance_matrix, xyz_centroid) == 0)
84 {
85 continue;
86 }
87
88 EIGEN_ALIGN16 Eigen::Matrix3f eigen_vector3;
89 EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
90 //计算特征值和特征向量
91 pcl::eigen33(covariance_matrix, eigen_vector3, eigen_values);
92 double eig_val1 = eigen_values[0];
93 double eig_val2 = eigen_values[1];
94 double eig_val3 = eigen_values[2];
95
96 output_pts[idx].x = input_->at((*indices_)[idx]).x;
97 output_pts[idx].y = input_->at((*indices_)[idx]).y;
98 output_pts[idx].z = input_->at((*indices_)[idx]).z;
99 output_pts[idx].eig_val1 = eig_val1;
100 output_pts[idx].eig_val2 = eig_val2;
101 output_pts[idx].eig_val3 = eig_val3;
102 }
103 QString savefilePly = filePly.replace(".ply",".txt");
104 std::wstring psaveFile1 = savefilePly.toStdWString();
105 char buffer[256];
106 size_t ret = wcstombs(buffer, psaveFile1.c_str(), sizeof(buffer));
107 const char * psavetxtFile = buffer;
108 char * file_name_2 = (char*)psavetxtFile;
109 FILE* saveFeaturePointCloud = fopen(file_name_2, "w");
110 for (int i = 0; i < output_pts.size(); i++)
111 {
112 float x = output_pts[i].x;
113 float y = output_pts[i].y;
114 float z = output_pts[i].z;
115
116 //注意：eig_val1最小
117 float eig_val1 = output_pts[i].eig_val1;
118 float eig_val2 = output_pts[i].eig_val2;
119 float eig_val3 = output_pts[i].eig_val3;
120 float eig_sum = eig_val1 + eig_val2 + eig_val3;
121
122 float e1 = 0, e2 = 0, e3 = 0;
123 float Linearity = 0;
124 float Planarity = 0;
125 float Scattering = 0;
126 float Omnivariance = 0;
127 float Anisotropy = 0;
128 float EigenEntropy = 0;
129 float changeOfcurvature = 0;
130 if (eig_sum != 0)
131 {
132 e1 = eig_val3 / eig_sum;
133 e2 = eig_val2 / eig_sum;
134 e3 = eig_val1 / eig_sum;
135 Linearity = (e1 - e2) / e1;
136 Planarity = (e2 - e3) / e1;
137 Scattering = e3 / e1;
138 Omnivariance = pow(e1*e2*e3, 1 / 3);
139 Anisotropy = (e1 - e3) / e1;
140 EigenEntropy = -(e1*log(e1) + e2*log(e2) + e3*log(e3));
141 //计算曲率变化
142 changeOfcurvature = fabsf(e1 / (e1 + e2 + e3));
143 }
144 else
145 changeOfcurvature = 0;
146 //x,y,z,e1,e2,e3,
147 //Linearity,Planarity,Scattering,Omnivariance,Anisotropy,
148 //Eigenentropy,Sum of eigenvalues,Change of curvature
149 fprintf(saveFeaturePointCloud, "%f %f %f %f %f %f %f %f %f %f %f %f %f %f\n", x, y, z, eig_val1, eig_val2, eig_val3,
150 Linearity, Planarity, Scattering, Omnivariance, Anisotropy, EigenEntropy, eig_sum, changeOfcurvature);
151 }
152 fclose(saveFeaturePointCloud);
153 }
