normal_refinement.hpp
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#ifndef PCL_FILTERS_IMPL_NORMAL_REFINEMENT_H_
#define PCL_FILTERS_IMPL_NORMAL_REFINEMENT_H_
#include <pcl/filters/normal_refinement.h>
///////////////////////////////////////////////////////////////////////////////////////////
template <typename NormalT> void
pcl::NormalRefinement<NormalT>::applyFilter (PointCloud &output)
{
// Check input
if (input_->empty ())
{
PCL_ERROR ("[pcl::%s::applyFilter] No source was input!\n",
getClassName ().c_str ());
}
// Copy to output
output = *input_;
// Check that correspondences are non-empty
if (k_indices_.empty () || k_sqr_distances_.empty ())
{
PCL_ERROR ("[pcl::%s::applyFilter] No point correspondences given! Returning original input.\n",
getClassName ().c_str ());
return;
}
// Check that correspondences are OK
const unsigned int size = k_indices_.size ();
if (k_sqr_distances_.size () != size || input_->size () != size)
{
PCL_ERROR ("[pcl::%s::applyFilter] Inconsistency between size of correspondence indices/distances or input! Returning original input.\n",
getClassName ().c_str ());
return;
}
// Run refinement while monitoring convergence
for (unsigned int i = 0; i < max_iterations_; ++i)
{
// Output of the current iteration
PointCloud tmp = output;
// Mean change in direction, measured by dot products
float ddot = 0.0f;
// Loop over all points in current output and write refined normal to tmp
int num_valids = 0;
for(unsigned int j = 0; j < size; ++j)
{
// Point to write to
NormalT& tmpj = tmp[j];
// Refine
if (refineNormal (output, j, k_indices_[j], k_sqr_distances_[j], tmpj))
{
// Accumulate using similarity in direction between previous iteration and current
const NormalT& outputj = output[j];
ddot += tmpj.normal_x * outputj.normal_x + tmpj.normal_y * outputj.normal_y + tmpj.normal_z * outputj.normal_z;
++num_valids;
}
}
// Take mean of similarities
ddot /= static_cast<float> (num_valids);
// Negate to since we want an error measure to approach zero
ddot = 1.0f - ddot;
// Update output
output = tmp;
// Break if converged
if (ddot < convergence_threshold_)
{
PCL_DEBUG("[pcl::%s::applyFilter] Converged after %i iterations with mean error of %f.\n",
getClassName ().c_str (), i+1, ddot);
break;
}
}
}
#endif
pcl::NormalRefinement::applyFilter
void applyFilter(PointCloud &output) override
Filter a Point Cloud.
Definition: normal_refinement.hpp:48
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
pcl::refineNormal
bool refineNormal(const PointCloud< NormalT > &cloud, int index, const Indices &k_indices, const std::vector< float > &k_sqr_distances, NormalT &point)
Refine an indexed point based on its neighbors, this function only writes to the normal_* fields.
Definition: normal_refinement.h:83
pcl::Normal
A point structure representing normal coordinates and the surface curvature estimate.
Definition: point_types.hpp:900