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intensity_gradient.hpp
/*
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* Point Cloud Library (PCL) - www.pointclouds.org
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#pragma once
#include <pcl/features/intensity_gradient.h>
#include <pcl/common/point_tests.h> // for pcl::isFinite
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT, typename IntensitySelectorT> void
pcl::IntensityGradientEstimation <PointInT, PointNT, PointOutT, IntensitySelectorT>::computePointIntensityGradient (
const pcl::PointCloud <PointInT> &cloud, const pcl::Indices &indices,
const Eigen::Vector3f &point, float mean_intensity, const Eigen::Vector3f &normal, Eigen::Vector3f &gradient)
{
if (indices.size () < 3)
{
gradient[0] = gradient[1] = gradient[2] = std::numeric_limits<float>::quiet_NaN ();
return;
}
Eigen::Matrix3f A = Eigen::Matrix3f::Zero ();
Eigen::Vector3f b = Eigen::Vector3f::Zero ();
for (const auto &nn_index : indices)
{
PointInT p = cloud[nn_index];
if (!std::isfinite (p.x) ||
!std::isfinite (p.y) ||
!std::isfinite (p.z) ||
!std::isfinite (intensity_ (p)))
continue;
p.x -= point[0];
p.y -= point[1];
p.z -= point[2];
intensity_.demean (p, mean_intensity);
A (0, 0) += p.x * p.x;
A (0, 1) += p.x * p.y;
A (0, 2) += p.x * p.z;
A (1, 1) += p.y * p.y;
A (1, 2) += p.y * p.z;
A (2, 2) += p.z * p.z;
b[0] += p.x * intensity_ (p);
b[1] += p.y * intensity_ (p);
b[2] += p.z * intensity_ (p);
}
// Fill in the lower triangle of A
A (1, 0) = A (0, 1);
A (2, 0) = A (0, 2);
A (2, 1) = A (1, 2);
// Eigen::Vector3f x = A.colPivHouseholderQr ().solve (b);
Eigen::Vector3f eigen_values;
Eigen::Matrix3f eigen_vectors;
eigen33 (A, eigen_vectors, eigen_values);
b = eigen_vectors.transpose () * b;
if ( eigen_values (0) != 0)
b (0) /= eigen_values (0);
else
b (0) = 0;
if ( eigen_values (1) != 0)
b (1) /= eigen_values (1);
else
b (1) = 0;
if ( eigen_values (2) != 0)
b (2) /= eigen_values (2);
else
b (2) = 0;
Eigen::Vector3f x = eigen_vectors * b;
// if (A.col (0).squaredNorm () != 0)
// x [0] /= A.col (0).squaredNorm ();
// b -= x [0] * A.col (0);
//
//
// if (A.col (1).squaredNorm () != 0)
// x [1] /= A.col (1).squaredNorm ();
// b -= x[1] * A.col (1);
//
// x [2] = b.dot (A.col (2));
// if (A.col (2).squaredNorm () != 0)
// x[2] /= A.col (2).squaredNorm ();
// // Fit a hyperplane to the data
//
// std::cout << A << "\n*\n" << bb << "\n=\n" << x << "\nvs.\n" << x2 << "\n\n";
// std::cout << A * x << "\nvs.\n" << A * x2 << "\n\n------\n";
// Project the gradient vector, x, onto the tangent plane
gradient = (Eigen::Matrix3f::Identity () - normal*normal.transpose ()) * x;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT, typename IntensitySelectorT> void
pcl::IntensityGradientEstimation<PointInT, PointNT, PointOutT, IntensitySelectorT>::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;
// If the data is dense, we don't need to check for NaN
if (surface_->is_dense)
{
#pragma omp parallel for \
default(none) \
shared(output) \
firstprivate(nn_indices, nn_dists) \
num_threads(threads_)
// Iterating over the entire index vector
for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
{
PointOutT &p_out = output[idx];
if (!this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists))
{
p_out.gradient[0] = p_out.gradient[1] = p_out.gradient[2] = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
Eigen::Vector3f centroid;
float mean_intensity = 0;
// Initialize to 0
centroid.setZero ();
for (const auto &nn_index : nn_indices)
{
centroid += (*surface_)[nn_index].getVector3fMap ();
mean_intensity += intensity_ ((*surface_)[nn_index]);
}
centroid /= static_cast<float> (nn_indices.size ());
mean_intensity /= static_cast<float> (nn_indices.size ());
Eigen::Vector3f normal = Eigen::Vector3f::Map ((*normals_)[(*indices_) [idx]].normal);
Eigen::Vector3f gradient;
computePointIntensityGradient (*surface_, nn_indices, centroid, mean_intensity, normal, gradient);
p_out.gradient[0] = gradient[0];
p_out.gradient[1] = gradient[1];
p_out.gradient[2] = gradient[2];
}
}
else
{
#pragma omp parallel for \
default(none) \
shared(output) \
firstprivate(nn_indices, nn_dists) \
num_threads(threads_)
// Iterating over the entire index vector
for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
{
PointOutT &p_out = output[idx];
if (!isFinite ((*surface_) [(*indices_)[idx]]) ||
!this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists))
{
p_out.gradient[0] = p_out.gradient[1] = p_out.gradient[2] = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
Eigen::Vector3f centroid;
float mean_intensity = 0;
// Initialize to 0
centroid.setZero ();
unsigned cp = 0;
for (const auto &nn_index : nn_indices)
{
// Check if the point is invalid
if (!isFinite ((*surface_) [nn_index]))
continue;
centroid += surface_->points [nn_index].getVector3fMap ();
mean_intensity += intensity_ (surface_->points [nn_index]);
++cp;
}
centroid /= static_cast<float> (cp);
mean_intensity /= static_cast<float> (cp);
Eigen::Vector3f normal = Eigen::Vector3f::Map ((*normals_)[(*indices_) [idx]].normal);
Eigen::Vector3f gradient;
computePointIntensityGradient (*surface_, nn_indices, centroid, mean_intensity, normal, gradient);
p_out.gradient[0] = gradient[0];
p_out.gradient[1] = gradient[1];
p_out.gradient[2] = gradient[2];
}
}
}
#define PCL_INSTANTIATE_IntensityGradientEstimation(InT,NT,OutT) template class PCL_EXPORTS pcl::IntensityGradientEstimation<InT,NT,OutT>;
pcl::IntensityGradientEstimation::computePointIntensityGradient
void computePointIntensityGradient(const pcl::PointCloud< PointInT > &cloud, const pcl::Indices &indices, const Eigen::Vector3f &point, float mean_intensity, const Eigen::Vector3f &normal, Eigen::Vector3f &gradient)
Estimate the intensity gradient around a given point based on its spatial neighborhood of points.
Definition intensity_gradient.hpp:50
pcl::IntensityGradientEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the intensity gradients for a set of points given in <setInputCloud (), setIndices ()> using...
Definition intensity_gradient.hpp:143
pcl::IntensityGradientEstimation::PointCloudOut
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition intensity_gradient.h:69
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