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vfh.hpp
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#ifndef PCL_FEATURES_IMPL_VFH_H_
#define PCL_FEATURES_IMPL_VFH_H_
#include <pcl/features/vfh.h>
#include <pcl/features/pfh_tools.h>
#include <pcl/common/common.h>
#include <pcl/common/centroid.h>
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT> bool
pcl::VFHEstimation<PointInT, PointNT, PointOutT>::initCompute ()
{
if (input_->size () < 2 || (surface_ && surface_->size () < 2))
{
PCL_ERROR ("[pcl::VFHEstimation::initCompute] Input dataset must have at least 2 points!\n");
return (false);
}
if (search_radius_ == 0 && k_ == 0)
k_ = 1;
return (Feature<PointInT, PointOutT>::initCompute ());
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT> void
pcl::VFHEstimation<PointInT, PointNT, PointOutT>::compute (PointCloudOut &output)
{
if (!initCompute ())
{
output.width = output.height = 0;
output.clear ();
return;
}
// Copy the header
output.header = input_->header;
// Resize the output dataset
// Important! We should only allocate precisely how many elements we will need, otherwise
// we risk at pre-allocating too much memory which could lead to bad_alloc
// (see http://dev.pointclouds.org/issues/657)
output.width = output.height = 1;
output.is_dense = input_->is_dense;
output.resize (1);
// Perform the actual feature computation
computeFeature (output);
Feature<PointInT, PointOutT>::deinitCompute ();
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT> void
pcl::VFHEstimation<PointInT, PointNT, PointOutT>::computePointSPFHSignature (const Eigen::Vector4f &centroid_p,
const Eigen::Vector4f &centroid_n,
const pcl::PointCloud<PointInT> &cloud,
const pcl::PointCloud<PointNT> &normals,
const pcl::Indices &indices)
{
Eigen::Vector4f pfh_tuple;
// Reset the whole thing
for (int i = 0; i < 4; ++i)
{
hist_f_[i].setZero (nr_bins_f_[i]);
}
// Get the bounding box of the current cluster
//Eigen::Vector4f min_pt, max_pt;
//pcl::getMinMax3D (cloud, indices, min_pt, max_pt);
//double distance_normalization_factor = (std::max)((centroid_p - min_pt).norm (), (centroid_p - max_pt).norm ());
//Instead of using the bounding box to normalize the VFH distance component, it is better to use the max_distance
//from any point to centroid. VFH is invariant to rotation about the roll axis but the bounding box is not,
//resulting in different normalization factors for point clouds that are just rotated about that axis.
double distance_normalization_factor = 1.0;
if (normalize_distances_)
{
Eigen::Vector4f max_pt;
pcl::getMaxDistance (cloud, indices, centroid_p, max_pt);
max_pt[3] = 0;
distance_normalization_factor = (centroid_p - max_pt).norm ();
}
// Factorization constant
float hist_incr = 1;
if (normalize_bins_)
hist_incr = 100.0f / static_cast<float> (indices.size () - 1);
float hist_incr_size_component = 0;
if (size_component_)
hist_incr_size_component = hist_incr;
// Iterate over all the points in the neighborhood
for (const auto &index : indices)
{
// Compute the pair P to NNi
if (!computePairFeatures (centroid_p, centroid_n, cloud[index].getVector4fMap (),
normals[index].getNormalVector4fMap (), pfh_tuple[0], pfh_tuple[1],
pfh_tuple[2], pfh_tuple[3]))
continue;
// Normalize the f1, f2, f3, f4 features and push them in the histogram
for (int i = 0; i < 3; ++i)
{
const int raw_index = static_cast<int> (std::floor (nr_bins_f_[i] * ((pfh_tuple[i] + M_PI) * d_pi_)));
const int h_index = std::max(std::min(raw_index, nr_bins_f_[i] - 1), 0);
hist_f_[i] (h_index) += hist_incr;
}
if (hist_incr_size_component)
{
int h_index;
if (normalize_distances_)
h_index = static_cast<int> (std::floor (nr_bins_f_[3] * (pfh_tuple[3] / distance_normalization_factor)));
else
h_index = static_cast<int> (pcl_round (pfh_tuple[3] * 100));
h_index = std::max (std::min (h_index, nr_bins_f_[3] - 1), 0);
hist_f_[3] (h_index) += hist_incr_size_component;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::VFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
// ---[ Step 1a : compute the centroid in XYZ space
Eigen::Vector4f xyz_centroid (0, 0, 0, 0);
if (use_given_centroid_)
xyz_centroid = centroid_to_use_;
else
compute3DCentroid (*surface_, *indices_, xyz_centroid); // Estimate the XYZ centroid
// ---[ Step 1b : compute the centroid in normal space
Eigen::Vector4f normal_centroid = Eigen::Vector4f::Zero ();
// If the data is dense, we don't need to check for NaN
if (use_given_normal_)
normal_centroid = normal_to_use_;
else
{
std::size_t cp = 0;
if (normals_->is_dense)
{
for (const auto& index: *indices_)
{
normal_centroid.noalias () += (*normals_)[index].getNormalVector4fMap ();
}
cp = indices_->size();
}
// NaN or Inf values could exist => check for them
else
{
for (const auto& index: *indices_)
{
if (!std::isfinite ((*normals_)[index].normal[0]) ||
!std::isfinite ((*normals_)[index].normal[1]) ||
!std::isfinite ((*normals_)[index].normal[2]))
continue;
normal_centroid.noalias () += (*normals_)[index].getNormalVector4fMap ();
cp++;
}
}
normal_centroid /= static_cast<float> (cp);
}
// Compute the direction of view from the viewpoint to the centroid
Eigen::Vector4f viewpoint (vpx_, vpy_, vpz_, 0);
Eigen::Vector4f d_vp_p = viewpoint - xyz_centroid;
d_vp_p.normalize ();
// Estimate the SPFH at nn_indices[0] using the entire cloud
computePointSPFHSignature (xyz_centroid, normal_centroid, *surface_, *normals_, *indices_);
// ---[ Step 2 : obtain the viewpoint component
hist_vp_.setZero (nr_bins_vp_);
float hist_incr = 1.0;
if (normalize_bins_)
hist_incr = 100.0 / static_cast<double> (indices_->size ());
for (const auto& index: *indices_)
{
Eigen::Vector4f normal ((*normals_)[index].normal[0],
(*normals_)[index].normal[1],
(*normals_)[index].normal[2], 0);
// Normalize
double alpha = (normal.dot (d_vp_p) + 1.0) * 0.5;
auto fi = static_cast<std::size_t> (std::floor (alpha * hist_vp_.size ()));
fi = std::max<std::size_t> (0u, fi);
fi = std::min<std::size_t> (hist_vp_.size () - 1, fi);
// Bin into the histogram
hist_vp_ [fi] += hist_incr;
}
// We only output _1_ signature
output.resize (1);
output.width = 1;
output.height = 1;
// Estimate the FPFH at nn_indices[0] using the entire cloud and copy the resultant signature
auto outPtr = std::begin (output[0].histogram);
for (int i = 0; i < 4; ++i)
{
outPtr = std::copy (hist_f_[i].data (), hist_f_[i].data () + hist_f_[i].size (), outPtr);
}
outPtr = std::copy (hist_vp_.data (), hist_vp_.data () + hist_vp_.size (), outPtr);
}
#define PCL_INSTANTIATE_VFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::VFHEstimation<T,NT,OutT>;
#endif // PCL_FEATURES_IMPL_VFH_H_
centroid.h
Define methods for centroid estimation and covariance matrix calculus.
pcl::Feature
Feature represents the base feature class.
Definition feature.h:107
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::VFHEstimation
VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud data...
Definition vfh.h:73
pcl::VFHEstimation::PointCloudOut
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition vfh.h:84
pcl::VFHEstimation::compute
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition vfh.hpp:65
pcl::VFHEstimation::initCompute
bool initCompute() override
This method should get called before starting the actual computation.
Definition vfh.hpp:51
pcl::VFHEstimation::computePointSPFHSignature
void computePointSPFHSignature(const Eigen::Vector4f &centroid_p, const Eigen::Vector4f &centroid_n, const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, const pcl::Indices &indices)
Estimate the SPFH (Simple Point Feature Histograms) signatures of the angular (f1,...
Definition vfh.hpp:92
common.h
Define standard C methods and C++ classes that are common to all methods.
pcl::getMaxDistance
void getMaxDistance(const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
Get the point at maximum distance from a given point and a given pointcloud.
Definition common.hpp:197
pcl::compute3DCentroid
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition centroid.hpp:57
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::gpu::cp
int cp(int from, int to)
Returns field copy operation code.
Definition repacks.hpp:54
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl_round
__inline double pcl_round(double number)
Win32 doesn't seem to have rounding functions.
Definition pcl_macros.h:240
M_PI
#define M_PI
Definition pcl_macros.h:203