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radius_outlier_removal.hpp
/*
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#ifndef PCL_FILTERS_IMPL_RADIUS_OUTLIER_REMOVAL_H_
#define PCL_FILTERS_IMPL_RADIUS_OUTLIER_REMOVAL_H_
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/search/organized.h> // for OrganizedNeighbor
#include <pcl/search/kdtree.h> // for KdTree
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::RadiusOutlierRemoval<PointT>::applyFilterIndices (Indices &indices)
{
if (search_radius_ == 0.0)
{
PCL_ERROR ("[pcl::%s::applyFilter] No radius defined!\n", getClassName ().c_str ());
indices.clear ();
removed_indices_->clear ();
return;
}
// Initialize the search class
if (!searcher_)
{
if (input_->isOrganized ())
searcher_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
else
searcher_.reset (new pcl::search::KdTree<PointT> (false));
}
if (!searcher_->setInputCloud (input_))
{
PCL_ERROR ("[pcl::%s::applyFilter] Error when initializing search method!\n", getClassName ().c_str ());
indices.clear ();
removed_indices_->clear ();
return;
}
// Note: k includes the query point, so is always at least 1
const int mean_k = min_pts_radius_ + 1;
const double nn_dists_max = search_radius_ * search_radius_;
// The arrays to be used
Indices nn_indices (mean_k);
std::vector<float> nn_dists(mean_k);
// Set to keep all points and in the filtering set those we don't want to keep, assuming
// we want to keep the majority of the points.
// 0 = remove, 1 = keep
std::vector<std::uint8_t> to_keep(indices_->size(), 1);
indices.resize (indices_->size ());
removed_indices_->resize (indices_->size ());
int oii = 0, rii = 0; // oii = output indices iterator, rii = removed indices iterator
// If the data is dense => use nearest-k search
if (input_->is_dense)
{
#pragma omp parallel for \
schedule(dynamic,64) \
firstprivate(nn_indices, nn_dists) \
shared(to_keep) \
num_threads(num_threads_)
for (ptrdiff_t i = 0; i < static_cast<ptrdiff_t>(indices_->size()); i++)
{
const auto& index = (*indices_)[i];
// Perform the nearest-k search
const int k = searcher_->nearestKSearch (index, mean_k, nn_indices, nn_dists);
// Check the number of neighbors
// Note: nn_dists is sorted, so check the last item
if (k == mean_k)
{
// if negative_ is false and a neighbor is further away than max distance, remove the point
// or
// if negative is true and a neighbor is closer than max distance, remove the point
if ((!negative_ && nn_dists_max < nn_dists[k-1]) || (negative_ && nn_dists_max >= nn_dists[k - 1]))
{
to_keep[i] = 0;
continue;
}
}
else
{
if (!negative_)
{
// if too few neighbors, remove the point
to_keep[i] = 0;
continue;
}
}
}
}
// NaN or Inf values could exist => use radius search
else
{
#pragma omp parallel for \
schedule(dynamic, 64) \
firstprivate(nn_indices, nn_dists) \
shared(to_keep) \
num_threads(num_threads_)
for (ptrdiff_t i = 0; i < static_cast<ptrdiff_t>(indices_->size()); i++)
{
const auto& index = (*indices_)[i];
if (!pcl::isXYZFinite((*input_)[index]))
{
to_keep[i] = 0;
continue;
}
// Perform the radius search
// Note: k includes the query point, so is always at least 1
// last parameter (max_nn) is the maximum number of neighbors returned. If enough neighbors are found so that point can not be an outlier, we stop searching.
const int k = searcher_->radiusSearch (index, search_radius_, nn_indices, nn_dists, min_pts_radius_ + 1);
// Points having too few neighbors are removed
// or if negative_ is true, then if it has too many neighbors
if ((!negative_ && k <= min_pts_radius_) || (negative_ && k > min_pts_radius_))
{
to_keep[i] = 0;
continue;
}
}
}
for (index_t i=0; i < static_cast<index_t>(to_keep.size()); i++)
{
if (to_keep[i] == 0)
{
if (extract_removed_indices_)
(*removed_indices_)[rii++] = (*indices_)[i];
continue;
}
// Otherwise it was a normal point for output (inlier)
indices[oii++] = (*indices_)[i];
}
// Resize the output arrays
indices.resize (oii);
removed_indices_->resize (rii);
}
#define PCL_INSTANTIATE_RadiusOutlierRemoval(T) template class PCL_EXPORTS pcl::RadiusOutlierRemoval<T>;
#endif // PCL_FILTERS_IMPL_RADIUS_OUTLIER_REMOVAL_H_
pcl::RadiusOutlierRemoval::applyFilterIndices
void applyFilterIndices(Indices &indices)
Filtered results are indexed by an indices array.
Definition radius_outlier_removal.hpp:49
pcl::search::KdTree
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition kdtree.h:62
pcl::search::OrganizedNeighbor
OrganizedNeighbor is a class for optimized nearest neighbor search in organized projectable point clo...
Definition organized.h:66
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
pcl::isXYZFinite
constexpr bool isXYZFinite(const PointT &) noexcept
Definition point_tests.h:125