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principal_curvatures.hpp
/*
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#pragma once
#include <pcl/features/principal_curvatures.h>
#include <pcl/common/point_tests.h> // for pcl::isFinite
#include <pcl/common/eigen.h> // for eigen33
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::setNumberOfThreads (unsigned int nr_threads)
{
#ifdef _OPENMP
if (nr_threads == 0)
threads_ = omp_get_num_procs();
else
threads_ = nr_threads;
PCL_DEBUG ("[pcl::PrincipalCurvaturesEstimation::setNumberOfThreads] Setting number of threads to %u.\n", threads_);
#else
threads_ = 1;
if (nr_threads != 1)
PCL_WARN ("[pcl::PrincipalCurvaturesEstimation::setNumberOfThreads] Parallelization is requested, but OpenMP is not available! Continuing without parallelization.\n");
#endif // _OPENMP
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computePointPrincipalCurvatures (
const pcl::PointCloud<PointNT> &normals, int p_idx, const pcl::Indices &indices,
float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
{
const auto n_idx = normals[p_idx].getNormalVector3fMap();
EIGEN_ALIGN16 const Eigen::Matrix3f I = Eigen::Matrix3f::Identity ();
EIGEN_ALIGN16 const Eigen::Matrix3f M = I - n_idx * n_idx.transpose (); // projection matrix (into tangent plane)
// Project normals into the tangent plane
std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > projected_normals (indices.size());
Eigen::Vector3f xyz_centroid = Eigen::Vector3f::Zero();
for (std::size_t idx = 0; idx < indices.size(); ++idx)
{
const auto normal = normals[indices[idx]].getNormalVector3fMap();
projected_normals[idx] = M * normal;
xyz_centroid += projected_normals[idx];
}
// Estimate the XYZ centroid
xyz_centroid /= static_cast<float> (indices.size ());
// Initialize to 0
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix = Eigen::Matrix3f::Zero();
// For each point in the cloud
for (std::size_t idx = 0; idx < indices.size (); ++idx)
{
const Eigen::Vector3f demean = projected_normals[idx] - xyz_centroid;
const double demean_xy = demean[0] * demean[1];
const double demean_xz = demean[0] * demean[2];
const double demean_yz = demean[1] * demean[2];
covariance_matrix(0, 0) += demean[0] * demean[0];
covariance_matrix(0, 1) += static_cast<float> (demean_xy);
covariance_matrix(0, 2) += static_cast<float> (demean_xz);
covariance_matrix(1, 0) += static_cast<float> (demean_xy);
covariance_matrix(1, 1) += demean[1] * demean[1];
covariance_matrix(1, 2) += static_cast<float> (demean_yz);
covariance_matrix(2, 0) += static_cast<float> (demean_xz);
covariance_matrix(2, 1) += static_cast<float> (demean_yz);
covariance_matrix(2, 2) += demean[2] * demean[2];
}
// Extract the eigenvalues and eigenvectors
Eigen::Vector3f eigenvalues;
Eigen::Vector3f eigenvector;
pcl::eigen33 (covariance_matrix, eigenvalues);
pcl::computeCorrespondingEigenVector (covariance_matrix, eigenvalues [2], eigenvector);
pcx = eigenvector [0];
pcy = eigenvector [1];
pcz = eigenvector [2];
const float indices_size = 1.0f / static_cast<float> (indices.size ());
pc1 = eigenvalues [2] * indices_size;
pc2 = eigenvalues [1] * indices_size;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
// Allocate enough space to hold the results
// \note This resize is irrelevant for a radiusSearch ().
pcl::Indices nn_indices (k_);
std::vector<float> nn_dists (k_);
output.is_dense = true;
// Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
if (input_->is_dense)
{
#pragma omp parallel for \
default(none) \
shared(output) \
firstprivate(nn_indices, nn_dists) \
num_threads(threads_) \
schedule(dynamic, chunk_size_)
// Iterating over the entire index vector
for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
{
if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
{
output[idx].principal_curvature[0] = output[idx].principal_curvature[1] = output[idx].principal_curvature[2] =
output[idx].pc1 = output[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// Estimate the principal curvatures at each patch
computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
output[idx].principal_curvature[0], output[idx].principal_curvature[1], output[idx].principal_curvature[2],
output[idx].pc1, output[idx].pc2);
}
}
else
{
#pragma omp parallel for \
default(none) \
shared(output) \
firstprivate(nn_indices, nn_dists) \
num_threads(threads_) \
schedule(dynamic, chunk_size_)
// Iterating over the entire index vector
for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
{
if (!isFinite ((*input_)[(*indices_)[idx]]) ||
this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
{
output[idx].principal_curvature[0] = output[idx].principal_curvature[1] = output[idx].principal_curvature[2] =
output[idx].pc1 = output[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// Estimate the principal curvatures at each patch
computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
output[idx].principal_curvature[0], output[idx].principal_curvature[1], output[idx].principal_curvature[2],
output[idx].pc1, output[idx].pc2);
}
}
}
#define PCL_INSTANTIATE_PrincipalCurvaturesEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PrincipalCurvaturesEstimation<T,NT,OutT>;
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PrincipalCurvaturesEstimation::setNumberOfThreads
void setNumberOfThreads(unsigned int nr_threads)
Initialize the scheduler and set the number of threads to use.
Definition principal_curvatures.hpp:50
pcl::PrincipalCurvaturesEstimation::computePointPrincipalCurvatures
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...
Definition principal_curvatures.hpp:67
pcl::PrincipalCurvaturesEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1) a...
Definition principal_curvatures.hpp:130
pcl::PrincipalCurvaturesEstimation::PointCloudOut
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition principal_curvatures.h:72
pcl::computeCorrespondingEigenVector
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
pcl::eigen33
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
pcl::isFinite
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
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133