Point Cloud Library (PCL) 1.12.0
principal_curvatures.hpp
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40
41#pragma once
42
43#include <pcl/features/principal_curvatures.h>
44
45#include <pcl/common/point_tests.h> // for pcl::isFinite
46
47
48//////////////////////////////////////////////////////////////////////////////////////////////
49template <typename PointInT, typename PointNT, typename PointOutT> void
51 const pcl::PointCloud<PointNT> &normals, int p_idx, const pcl::Indices &indices,
52 float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
53{
54 EIGEN_ALIGN16 Eigen::Matrix3f I = Eigen::Matrix3f::Identity ();
55 Eigen::Vector3f n_idx (normals[p_idx].normal[0], normals[p_idx].normal[1], normals[p_idx].normal[2]);
56 EIGEN_ALIGN16 Eigen::Matrix3f M = I - n_idx * n_idx.transpose (); // projection matrix (into tangent plane)
57
58 // Project normals into the tangent plane
59 Eigen::Vector3f normal;
60 projected_normals_.resize (indices.size ());
61 xyz_centroid_.setZero ();
62 for (std::size_t idx = 0; idx < indices.size(); ++idx)
63 {
64 normal[0] = normals[indices[idx]].normal[0];
65 normal[1] = normals[indices[idx]].normal[1];
66 normal[2] = normals[indices[idx]].normal[2];
67
68 projected_normals_[idx] = M * normal;
69 xyz_centroid_ += projected_normals_[idx];
70 }
71
72 // Estimate the XYZ centroid
73 xyz_centroid_ /= static_cast<float> (indices.size ());
74
75 // Initialize to 0
76 covariance_matrix_.setZero ();
77
78 // For each point in the cloud
79 for (std::size_t idx = 0; idx < indices.size (); ++idx)
80 {
81 demean_ = projected_normals_[idx] - xyz_centroid_;
82
83 double demean_xy = demean_[0] * demean_[1];
84 double demean_xz = demean_[0] * demean_[2];
85 double demean_yz = demean_[1] * demean_[2];
86
87 covariance_matrix_(0, 0) += demean_[0] * demean_[0];
88 covariance_matrix_(0, 1) += static_cast<float> (demean_xy);
89 covariance_matrix_(0, 2) += static_cast<float> (demean_xz);
90
91 covariance_matrix_(1, 0) += static_cast<float> (demean_xy);
92 covariance_matrix_(1, 1) += demean_[1] * demean_[1];
93 covariance_matrix_(1, 2) += static_cast<float> (demean_yz);
94
95 covariance_matrix_(2, 0) += static_cast<float> (demean_xz);
96 covariance_matrix_(2, 1) += static_cast<float> (demean_yz);
97 covariance_matrix_(2, 2) += demean_[2] * demean_[2];
98 }
99
100 // Extract the eigenvalues and eigenvectors
101 pcl::eigen33 (covariance_matrix_, eigenvalues_);
102 pcl::computeCorrespondingEigenVector (covariance_matrix_, eigenvalues_ [2], eigenvector_);
103
104 pcx = eigenvector_ [0];
105 pcy = eigenvector_ [1];
106 pcz = eigenvector_ [2];
107 float indices_size = 1.0f / static_cast<float> (indices.size ());
108 pc1 = eigenvalues_ [2] * indices_size;
109 pc2 = eigenvalues_ [1] * indices_size;
110}
111
112
113//////////////////////////////////////////////////////////////////////////////////////////////
114template <typename PointInT, typename PointNT, typename PointOutT> void
116{
117 // Allocate enough space to hold the results
118 // \note This resize is irrelevant for a radiusSearch ().
119 pcl::Indices nn_indices (k_);
120 std::vector<float> nn_dists (k_);
121
122 output.is_dense = true;
123 // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
124 if (input_->is_dense)
125 {
126 // Iterating over the entire index vector
127 for (std::size_t idx = 0; idx < indices_->size (); ++idx)
128 {
129 if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
130 {
131 output[idx].principal_curvature[0] = output[idx].principal_curvature[1] = output[idx].principal_curvature[2] =
132 output[idx].pc1 = output[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
133 output.is_dense = false;
134 continue;
135 }
136
137 // Estimate the principal curvatures at each patch
138 computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
139 output[idx].principal_curvature[0], output[idx].principal_curvature[1], output[idx].principal_curvature[2],
140 output[idx].pc1, output[idx].pc2);
141 }
142 }
143 else
144 {
145 // Iterating over the entire index vector
146 for (std::size_t idx = 0; idx < indices_->size (); ++idx)
147 {
148 if (!isFinite ((*input_)[(*indices_)[idx]]) ||
149 this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
150 {
151 output[idx].principal_curvature[0] = output[idx].principal_curvature[1] = output[idx].principal_curvature[2] =
152 output[idx].pc1 = output[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
153 output.is_dense = false;
154 continue;
155 }
156
157 // Estimate the principal curvatures at each patch
158 computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
159 output[idx].principal_curvature[0], output[idx].principal_curvature[1], output[idx].principal_curvature[2],
160 output[idx].pc1, output[idx].pc2);
161 }
162 }
163}
164
165#define PCL_INSTANTIATE_PrincipalCurvaturesEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PrincipalCurvaturesEstimation<T,NT,OutT>;
166
void computePointPrincipalCurvatures(const pcl::PointCloud< PointNT > &normals, int p_idx, const pcl::Indices &indices, float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
Perform Principal Components Analysis (PCA) on the point normals of a surface patch in the tangent pl...
void computeFeature(PointCloudOut &output) override
Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1) a...
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
void computeCorrespondingEigenVector(const Matrix &mat, const typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi defin...
Definition: eigen.hpp:226
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition: eigen.hpp:296
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition: point_tests.h:55
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133