principal_curvatures.hpp
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#pragma once
#include <pcl/features/principal_curvatures.h>
#include <pcl/common/point_tests.h> // for pcl::isFinite
//////////////////////////////////////////////////////////////////////////////////////////////
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)
{
EIGEN_ALIGN16 Eigen::Matrix3f I = Eigen::Matrix3f::Identity ();
Eigen::Vector3f n_idx (normals[p_idx].normal[0], normals[p_idx].normal[1], normals[p_idx].normal[2]);
EIGEN_ALIGN16 Eigen::Matrix3f M = I - n_idx * n_idx.transpose (); // projection matrix (into tangent plane)
// Project normals into the tangent plane
Eigen::Vector3f normal;
projected_normals_.resize (indices.size ());
xyz_centroid_.setZero ();
for (std::size_t idx = 0; idx < indices.size(); ++idx)
{
normal[0] = normals[indices[idx]].normal[0];
normal[1] = normals[indices[idx]].normal[1];
normal[2] = normals[indices[idx]].normal[2];
projected_normals_[idx] = M * normal;
xyz_centroid_ += projected_normals_[idx];
}
// Estimate the XYZ centroid
xyz_centroid_ /= static_cast<float> (indices.size ());
// Initialize to 0
covariance_matrix_.setZero ();
// For each point in the cloud
for (std::size_t idx = 0; idx < indices.size (); ++idx)
{
demean_ = projected_normals_[idx] - xyz_centroid_;
double demean_xy = demean_[0] * demean_[1];
double demean_xz = demean_[0] * demean_[2];
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
pcl::eigen33 (covariance_matrix_, eigenvalues_);
pcl::computeCorrespondingEigenVector (covariance_matrix_, eigenvalues_ [2], eigenvector_);
pcx = eigenvector_ [0];
pcy = eigenvector_ [1];
pcz = eigenvector_ [2];
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)
{
// Iterating over the entire index vector
for (std::size_t idx = 0; idx < 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
{
// Iterating over the entire index vector
for (std::size_t idx = 0; idx < 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< PointNT >
pcl::PointCloud::is_dense
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:403
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:50
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:115
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:296
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