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intensity_spin.hpp
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#ifndef PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
#define PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
#include <pcl/features/intensity_spin.h>
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::IntensitySpinEstimation<PointInT, PointOutT>::computeIntensitySpinImage (
const PointCloudIn &cloud, float radius, float sigma,
int k,
const pcl::Indices &indices,
const std::vector<float> &squared_distances,
Eigen::MatrixXf &intensity_spin_image)
{
// Determine the number of bins to use based on the size of intensity_spin_image
int nr_distance_bins = static_cast<int> (intensity_spin_image.cols ());
int nr_intensity_bins = static_cast<int> (intensity_spin_image.rows ());
// Find the min and max intensity values in the given neighborhood
float min_intensity = std::numeric_limits<float>::max ();
float max_intensity = -std::numeric_limits<float>::max ();
for (int idx = 0; idx < k; ++idx)
{
min_intensity = (std::min) (min_intensity, cloud[indices[idx]].intensity);
max_intensity = (std::max) (max_intensity, cloud[indices[idx]].intensity);
}
float constant = 1.0f / (2.0f * sigma_ * sigma_);
// Compute the intensity spin image
intensity_spin_image.setZero ();
for (int idx = 0; idx < k; ++idx)
{
// Normalize distance and intensity values to: 0.0 <= d,i < nr_distance_bins,nr_intensity_bins
const float eps = std::numeric_limits<float>::epsilon ();
float d = static_cast<float> (nr_distance_bins) * std::sqrt (squared_distances[idx]) / (radius + eps);
float i = static_cast<float> (nr_intensity_bins) *
(cloud[indices[idx]].intensity - min_intensity) / (max_intensity - min_intensity + eps);
if (sigma == 0)
{
// If sigma is zero, update the histogram with no smoothing kernel
int d_idx = static_cast<int> (d);
int i_idx = static_cast<int> (i);
intensity_spin_image (i_idx, d_idx) += 1;
}
else
{
// Compute the bin indices that need to be updated (+/- 3 standard deviations)
int d_idx_min = (std::max)(static_cast<int> (std::floor (d - 3*sigma)), 0);
int d_idx_max = (std::min)(static_cast<int> (std::ceil (d + 3*sigma)), nr_distance_bins - 1);
int i_idx_min = (std::max)(static_cast<int> (std::floor (i - 3*sigma)), 0);
int i_idx_max = (std::min)(static_cast<int> (std::ceil (i + 3*sigma)), nr_intensity_bins - 1);
// Update the appropriate bins of the histogram
for (int i_idx = i_idx_min; i_idx <= i_idx_max; ++i_idx)
{
for (int d_idx = d_idx_min; d_idx <= d_idx_max; ++d_idx)
{
// Compute a "soft" update weight based on the distance between the point and the bin
float w = std::exp (-powf (d - static_cast<float> (d_idx), 2.0f) * constant - powf (i - static_cast<float> (i_idx), 2.0f) * constant);
intensity_spin_image (i_idx, d_idx) += w;
}
}
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::IntensitySpinEstimation<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
// Make sure a search radius is set
if (search_radius_ == 0.0)
{
PCL_ERROR ("[pcl::%s::computeFeature] The search radius must be set before computing the feature!\n",
getClassName ().c_str ());
output.width = output.height = 0;
output.clear ();
return;
}
// Make sure the spin image has valid dimensions
if (nr_intensity_bins_ <= 0)
{
PCL_ERROR ("[pcl::%s::computeFeature] The number of intensity bins must be greater than zero!\n",
getClassName ().c_str ());
output.width = output.height = 0;
output.clear ();
return;
}
if (nr_distance_bins_ <= 0)
{
PCL_ERROR ("[pcl::%s::computeFeature] The number of distance bins must be greater than zero!\n",
getClassName ().c_str ());
output.width = output.height = 0;
output.clear ();
return;
}
Eigen::MatrixXf intensity_spin_image (nr_intensity_bins_, nr_distance_bins_);
// Allocate enough space to hold the radiusSearch results
pcl::Indices nn_indices (surface_->size ());
std::vector<float> nn_dist_sqr (surface_->size ());
output.is_dense = true;
// Iterating over the entire index vector
for (std::size_t idx = 0; idx < indices_->size (); ++idx)
{
// Find neighbors within the search radius
// TODO: do we want to use searchForNeighbors instead?
int k = tree_->radiusSearch ((*indices_)[idx], search_radius_, nn_indices, nn_dist_sqr);
if (k == 0)
{
for (int bin = 0; bin < nr_intensity_bins_ * nr_distance_bins_; ++bin)
output[idx].histogram[bin] = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// Compute the intensity spin image
computeIntensitySpinImage (*surface_, static_cast<float> (search_radius_), sigma_, k, nn_indices, nn_dist_sqr, intensity_spin_image);
// Copy into the resultant cloud
std::size_t bin = 0;
for (Eigen::Index bin_j = 0; bin_j < intensity_spin_image.cols (); ++bin_j)
for (Eigen::Index bin_i = 0; bin_i < intensity_spin_image.rows (); ++bin_i)
output[idx].histogram[bin++] = intensity_spin_image (bin_i, bin_j);
}
}
#define PCL_INSTANTIATE_IntensitySpinEstimation(T,NT) template class PCL_EXPORTS pcl::IntensitySpinEstimation<T,NT>;
#endif // PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
pcl::IntensitySpinEstimation::PointCloudOut
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition intensity_spin.h:74
pcl::IntensitySpinEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the intensity-domain descriptors at a set of points given by <setInputCloud (),...
Definition intensity_spin.hpp:110
pcl::IntensitySpinEstimation::computeIntensitySpinImage
void computeIntensitySpinImage(const PointCloudIn &cloud, float radius, float sigma, int k, const pcl::Indices &indices, const std::vector< float > &squared_distances, Eigen::MatrixXf &intensity_spin_image)
Estimate the intensity-domain spin image descriptor for a given point based on its spatial neighborho...
Definition intensity_spin.hpp:48
pcl::PointCloud< PointInT >
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133