Point Cloud Library (PCL) 1.15.0
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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#include <pcl/common/eigen.h> // for eigen33
47
48///////////////////////////////////////////////////////////////////////////////////////////
49template <typename PointInT, typename PointNT, typename PointOutT> void
51{
52#ifdef _OPENMP
53 if (nr_threads == 0)
54 threads_ = omp_get_num_procs();
55 else
56 threads_ = nr_threads;
57 PCL_DEBUG ("[pcl::PrincipalCurvaturesEstimation::setNumberOfThreads] Setting number of threads to %u.\n", threads_);
58#else
59 threads_ = 1;
60 if (nr_threads != 1)
61 PCL_WARN ("[pcl::PrincipalCurvaturesEstimation::setNumberOfThreads] Parallelization is requested, but OpenMP is not available! Continuing without parallelization.\n");
62#endif // _OPENMP
63}
64
65//////////////////////////////////////////////////////////////////////////////////////////////
66template <typename PointInT, typename PointNT, typename PointOutT> void
68 const pcl::PointCloud<PointNT> &normals, int p_idx, const pcl::Indices &indices,
69 float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
70{
71 const auto n_idx = normals[p_idx].getNormalVector3fMap();
72 EIGEN_ALIGN16 const Eigen::Matrix3f I = Eigen::Matrix3f::Identity ();
73 EIGEN_ALIGN16 const Eigen::Matrix3f M = I - n_idx * n_idx.transpose (); // projection matrix (into tangent plane)
74
75 // Project normals into the tangent plane
76 std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > projected_normals (indices.size());
77 Eigen::Vector3f xyz_centroid = Eigen::Vector3f::Zero();
78 for (std::size_t idx = 0; idx < indices.size(); ++idx)
79 {
80 const auto normal = normals[indices[idx]].getNormalVector3fMap();
81 projected_normals[idx] = M * normal;
82 xyz_centroid += projected_normals[idx];
83 }
84
85 // Estimate the XYZ centroid
86 xyz_centroid /= static_cast<float> (indices.size ());
87
88 // Initialize to 0
89 EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix = Eigen::Matrix3f::Zero();
90
91 // For each point in the cloud
92 for (std::size_t idx = 0; idx < indices.size (); ++idx)
93 {
94 const Eigen::Vector3f demean = projected_normals[idx] - xyz_centroid;
95
96 const double demean_xy = demean[0] * demean[1];
97 const double demean_xz = demean[0] * demean[2];
98 const double demean_yz = demean[1] * demean[2];
99
100 covariance_matrix(0, 0) += demean[0] * demean[0];
101 covariance_matrix(0, 1) += static_cast<float> (demean_xy);
102 covariance_matrix(0, 2) += static_cast<float> (demean_xz);
103
104 covariance_matrix(1, 0) += static_cast<float> (demean_xy);
105 covariance_matrix(1, 1) += demean[1] * demean[1];
106 covariance_matrix(1, 2) += static_cast<float> (demean_yz);
107
108 covariance_matrix(2, 0) += static_cast<float> (demean_xz);
109 covariance_matrix(2, 1) += static_cast<float> (demean_yz);
110 covariance_matrix(2, 2) += demean[2] * demean[2];
111 }
112
113 // Extract the eigenvalues and eigenvectors
114 Eigen::Vector3f eigenvalues;
115 Eigen::Vector3f eigenvector;
116 pcl::eigen33 (covariance_matrix, eigenvalues);
117 pcl::computeCorrespondingEigenVector (covariance_matrix, eigenvalues [2], eigenvector);
118
119 pcx = eigenvector [0];
120 pcy = eigenvector [1];
121 pcz = eigenvector [2];
122 const float indices_size = 1.0f / static_cast<float> (indices.size ());
123 pc1 = eigenvalues [2] * indices_size;
124 pc2 = eigenvalues [1] * indices_size;
125}
126
127
128//////////////////////////////////////////////////////////////////////////////////////////////
129template <typename PointInT, typename PointNT, typename PointOutT> void
131{
132 // Allocate enough space to hold the results
133 // \note This resize is irrelevant for a radiusSearch ().
134 pcl::Indices nn_indices (k_);
135 std::vector<float> nn_dists (k_);
136
137 output.is_dense = true;
138 // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
139 if (input_->is_dense)
140 {
141#pragma omp parallel for \
142 default(none) \
143 shared(output) \
144 firstprivate(nn_indices, nn_dists) \
145 num_threads(threads_) \
146 schedule(dynamic, chunk_size_)
147 // Iterating over the entire index vector
148 for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
149 {
150 if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
151 {
152 output[idx].principal_curvature[0] = output[idx].principal_curvature[1] = output[idx].principal_curvature[2] =
153 output[idx].pc1 = output[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
154 output.is_dense = false;
155 continue;
156 }
157
158 // Estimate the principal curvatures at each patch
159 computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
160 output[idx].principal_curvature[0], output[idx].principal_curvature[1], output[idx].principal_curvature[2],
161 output[idx].pc1, output[idx].pc2);
162 }
163 }
164 else
165 {
166#pragma omp parallel for \
167 default(none) \
168 shared(output) \
169 firstprivate(nn_indices, nn_dists) \
170 num_threads(threads_) \
171 schedule(dynamic, chunk_size_)
172 // Iterating over the entire index vector
173 for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
174 {
175 if (!isFinite ((*input_)[(*indices_)[idx]]) ||
176 this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
177 {
178 output[idx].principal_curvature[0] = output[idx].principal_curvature[1] = output[idx].principal_curvature[2] =
179 output[idx].pc1 = output[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
180 output.is_dense = false;
181 continue;
182 }
183
184 // Estimate the principal curvatures at each patch
185 computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
186 output[idx].principal_curvature[0], output[idx].principal_curvature[1], output[idx].principal_curvature[2],
187 output[idx].pc1, output[idx].pc2);
188 }
189 }
190}
191
192#define PCL_INSTANTIATE_PrincipalCurvaturesEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PrincipalCurvaturesEstimation<T,NT,OutT>;
193
PointCloud represents the base class in PCL for storing collections of 3D points.
void setNumberOfThreads(unsigned int nr_threads)
Initialize the scheduler and set the number of threads to use.
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:295
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