fpfh.hpp
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#pragma once
#include <pcl/features/fpfh.h>
#include <pcl/common/point_tests.h> // for pcl::isFinite
#include <pcl/features/pfh_tools.h>
#include <set> // for std::set
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> bool
pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::computePairFeatures (
const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
{
pcl::computePairFeatures (cloud[p_idx].getVector4fMap (), normals[p_idx].getNormalVector4fMap (),
cloud[q_idx].getVector4fMap (), normals[q_idx].getNormalVector4fMap (),
f1, f2, f3, f4);
return (true);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::computePointSPFHSignature (
const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
pcl::index_t p_idx, int row, const pcl::Indices &indices,
Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3)
{
Eigen::Vector4f pfh_tuple;
// Get the number of bins from the histograms size
// @TODO: use arrays
int nr_bins_f1 = static_cast<int> (hist_f1.cols ());
int nr_bins_f2 = static_cast<int> (hist_f2.cols ());
int nr_bins_f3 = static_cast<int> (hist_f3.cols ());
// Factorization constant
float hist_incr = 100.0f / static_cast<float>(indices.size () - 1);
// Iterate over all the points in the neighborhood
for (const auto &index : indices)
{
// Avoid unnecessary returns
if (p_idx == index)
continue;
// Compute the pair P to NNi
if (!computePairFeatures (cloud, normals, p_idx, index, pfh_tuple[0], pfh_tuple[1], pfh_tuple[2], pfh_tuple[3]))
continue;
// Normalize the f1, f2, f3 features and push them in the histogram
int h_index = static_cast<int> (std::floor (nr_bins_f1 * ((pfh_tuple[0] + M_PI) * d_pi_)));
if (h_index < 0) h_index = 0;
if (h_index >= nr_bins_f1) h_index = nr_bins_f1 - 1;
hist_f1 (row, h_index) += hist_incr;
h_index = static_cast<int> (std::floor (nr_bins_f2 * ((pfh_tuple[1] + 1.0) * 0.5)));
if (h_index < 0) h_index = 0;
if (h_index >= nr_bins_f2) h_index = nr_bins_f2 - 1;
hist_f2 (row, h_index) += hist_incr;
h_index = static_cast<int> (std::floor (nr_bins_f3 * ((pfh_tuple[2] + 1.0) * 0.5)));
if (h_index < 0) h_index = 0;
if (h_index >= nr_bins_f3) h_index = nr_bins_f3 - 1;
hist_f3 (row, h_index) += hist_incr;
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::weightPointSPFHSignature (
const Eigen::MatrixXf &hist_f1, const Eigen::MatrixXf &hist_f2, const Eigen::MatrixXf &hist_f3,
const pcl::Indices &indices, const std::vector<float> &dists, Eigen::VectorXf &fpfh_histogram)
{
assert (indices.size () == dists.size ());
// @TODO: use arrays
double sum_f1 = 0.0, sum_f2 = 0.0, sum_f3 = 0.0;
float weight = 0.0, val_f1, val_f2, val_f3;
// Get the number of bins from the histograms size
const auto nr_bins_f1 = hist_f1.cols ();
const auto nr_bins_f2 = hist_f2.cols ();
const auto nr_bins_f3 = hist_f3.cols ();
const auto nr_bins_f12 = nr_bins_f1 + nr_bins_f2;
// Clear the histogram
fpfh_histogram.setZero (nr_bins_f1 + nr_bins_f2 + nr_bins_f3);
// Use the entire patch
for (std::size_t idx = 0; idx < indices.size (); ++idx)
{
// Minus the query point itself
if (dists[idx] == 0)
continue;
// Standard weighting function used
weight = 1.0f / dists[idx];
// Weight the SPFH of the query point with the SPFH of its neighbors
for (Eigen::MatrixXf::Index f1_i = 0; f1_i < nr_bins_f1; ++f1_i)
{
val_f1 = hist_f1 (indices[idx], f1_i) * weight;
sum_f1 += val_f1;
fpfh_histogram[f1_i] += val_f1;
}
for (Eigen::MatrixXf::Index f2_i = 0; f2_i < nr_bins_f2; ++f2_i)
{
val_f2 = hist_f2 (indices[idx], f2_i) * weight;
sum_f2 += val_f2;
fpfh_histogram[f2_i + nr_bins_f1] += val_f2;
}
for (Eigen::MatrixXf::Index f3_i = 0; f3_i < nr_bins_f3; ++f3_i)
{
val_f3 = hist_f3 (indices[idx], f3_i) * weight;
sum_f3 += val_f3;
fpfh_histogram[f3_i + nr_bins_f12] += val_f3;
}
}
if (sum_f1 != 0)
sum_f1 = 100.0 / sum_f1; // histogram values sum up to 100
if (sum_f2 != 0)
sum_f2 = 100.0 / sum_f2; // histogram values sum up to 100
if (sum_f3 != 0)
sum_f3 = 100.0 / sum_f3; // histogram values sum up to 100
// Adjust final FPFH values
const auto denormalize_with = [](auto factor)
{
return [=](const auto& data) { return data * factor; };
};
auto last = fpfh_histogram.data ();
last = std::transform(last, last + nr_bins_f1, last, denormalize_with (sum_f1));
last = std::transform(last, last + nr_bins_f2, last, denormalize_with (sum_f2));
std::transform(last, last + nr_bins_f3, last, denormalize_with (sum_f3));
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::computeSPFHSignatures (std::vector<int> &spfh_hist_lookup,
Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3)
{
// Allocate enough space to hold the NN search results
// \note This resize is irrelevant for a radiusSearch ().
pcl::Indices nn_indices (k_);
std::vector<float> nn_dists (k_);
std::set<int> spfh_indices;
spfh_hist_lookup.resize (surface_->size ());
// Build a list of (unique) indices for which we will need to compute SPFH signatures
// (We need an SPFH signature for every point that is a neighbor of any point in input_[indices_])
if (surface_ != input_ ||
indices_->size () != surface_->size ())
{
for (const auto& p_idx: *indices_)
{
if (this->searchForNeighbors (p_idx, search_parameter_, nn_indices, nn_dists) == 0)
continue;
spfh_indices.insert (nn_indices.begin (), nn_indices.end ());
}
}
else
{
// Special case: When a feature must be computed at every point, there is no need for a neighborhood search
for (std::size_t idx = 0; idx < indices_->size (); ++idx)
spfh_indices.insert (static_cast<int> (idx));
}
// Initialize the arrays that will store the SPFH signatures
std::size_t data_size = spfh_indices.size ();
hist_f1.setZero (data_size, nr_bins_f1_);
hist_f2.setZero (data_size, nr_bins_f2_);
hist_f3.setZero (data_size, nr_bins_f3_);
// Compute SPFH signatures for every point that needs them
std::size_t i = 0;
for (const auto& p_idx: spfh_indices)
{
// Find the neighborhood around p_idx
if (this->searchForNeighbors (*surface_, p_idx, search_parameter_, nn_indices, nn_dists) == 0)
continue;
// Estimate the SPFH signature around p_idx
computePointSPFHSignature (*surface_, *normals_, p_idx, i, nn_indices, hist_f1, hist_f2, hist_f3);
// Populate a lookup table for converting a point index to its corresponding row in the spfh_hist_* matrices
spfh_hist_lookup[p_idx] = i;
i++;
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
// Allocate enough space to hold the NN search results
// \note This resize is irrelevant for a radiusSearch ().
pcl::Indices nn_indices (k_);
std::vector<float> nn_dists (k_);
std::vector<int> spfh_hist_lookup;
computeSPFHSignatures (spfh_hist_lookup, hist_f1_, hist_f2_, hist_f3_);
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)
{
// Iterate 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)
{
for (Eigen::Index d = 0; d < fpfh_histogram_.size (); ++d)
output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// ... and remap the nn_indices values so that they represent row indices in the spfh_hist_* matrices
// instead of indices into surface_->points
for (auto &nn_index : nn_indices)
nn_index = spfh_hist_lookup[nn_index];
// Compute the FPFH signature (i.e. compute a weighted combination of local SPFH signatures) ...
weightPointSPFHSignature (hist_f1_, hist_f2_, hist_f3_, nn_indices, nn_dists, fpfh_histogram_);
// ...and copy it into the output cloud
std::copy_n(fpfh_histogram_.data (), fpfh_histogram_.size (), output[idx].histogram);
}
}
else
{
// Iterate 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)
{
for (Eigen::Index d = 0; d < fpfh_histogram_.size (); ++d)
output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// ... and remap the nn_indices values so that they represent row indices in the spfh_hist_* matrices
// instead of indices into surface_->points
for (auto &nn_index : nn_indices)
nn_index = spfh_hist_lookup[nn_index];
// Compute the FPFH signature (i.e. compute a weighted combination of local SPFH signatures) ...
weightPointSPFHSignature (hist_f1_, hist_f2_, hist_f3_, nn_indices, nn_dists, fpfh_histogram_);
// ...and copy it into the output cloud
std::copy_n(fpfh_histogram_.data (), fpfh_histogram_.size (), output[idx].histogram);
}
}
}
#define PCL_INSTANTIATE_FPFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::FPFHEstimation<T,NT,OutT>;
pcl::FPFHEstimation::computeSPFHSignatures
void computeSPFHSignatures(std::vector< int > &spf_hist_lookup, Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3)
Estimate the set of all SPFH (Simple Point Feature Histograms) signatures for the input cloud.
Definition: fpfh.hpp:182
pcl::FPFHEstimation::computePointSPFHSignature
void computePointSPFHSignature(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, pcl::index_t p_idx, int row, const pcl::Indices &indices, Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3)
Estimate the SPFH (Simple Point Feature Histograms) individual signatures of the three angular (f1,...
Definition: fpfh.hpp:64
pcl::FPFHEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the Fast Point Feature Histograms (FPFH) descriptors at a set of points given by <setInputCl...
Definition: fpfh.hpp:238
pcl::FPFHEstimation::computePairFeatures
bool computePairFeatures(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
Definition: fpfh.hpp:52
pcl::FPFHEstimation::weightPointSPFHSignature
void weightPointSPFHSignature(const Eigen::MatrixXf &hist_f1, const Eigen::MatrixXf &hist_f2, const Eigen::MatrixXf &hist_f3, const pcl::Indices &indices, const std::vector< float > &dists, Eigen::VectorXf &fpfh_histogram)
Weight the SPFH (Simple Point Feature Histograms) individual histograms to create the final FPFH (Fas...
Definition: fpfh.hpp:110
pcl::PointCloud< PointInT >
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::computePairFeatures
PCL_EXPORTS bool computePairFeatures(const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
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::index_t
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition: types.h:112
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
M_PI
#define M_PI
Definition: pcl_macros.h:201