correspondence_estimation_backprojection.hpp

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#ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_
#define PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_
 
#include <pcl/common/copy_point.h>
 
namespace pcl {
 
namespace registration {
 
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
bool
CorrespondenceEstimationBackProjection<PointSource, PointTarget, NormalT, Scalar>::
    initCompute()
{
  if (!source_normals_ || !target_normals_) {
    PCL_WARN("[pcl::registration::%s::initCompute] Datasets containing normals for "
             "source/target have not been given!\n",
             getClassName().c_str());
    return (false);
  }
 
  return (
      CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::initCompute());
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
void
CorrespondenceEstimationBackProjection<PointSource, PointTarget, NormalT, Scalar>::
    determineCorrespondences(pcl::Correspondences& correspondences, double max_distance)
{
  if (!initCompute())
    return;
 
  correspondences.resize(indices_->size());
 
  pcl::Indices nn_indices(k_);
  std::vector<float> nn_dists(k_);
 
  int min_index = 0;
 
  pcl::Correspondence corr;
  unsigned int nr_valid_correspondences = 0;
 
  // Iterate over the input set of source indices
  for (const auto& idx_i : (*indices_)) {
    const auto& pt = detail::pointCopyOrRef<PointTarget, PointSource>(input_, idx_i);
    tree_->nearestKSearch(pt, k_, nn_indices, nn_dists);
 
    // Among the K nearest neighbours find the one with minimum perpendicular distance
    // to the normal
    float min_dist = std::numeric_limits<float>::max();
 
    // Find the best correspondence
    for (std::size_t j = 0; j < nn_indices.size(); j++) {
      float cos_angle = (*source_normals_)[idx_i].normal_x *
                            (*target_normals_)[nn_indices[j]].normal_x +
                        (*source_normals_)[idx_i].normal_y *
                            (*target_normals_)[nn_indices[j]].normal_y +
                        (*source_normals_)[idx_i].normal_z *
                            (*target_normals_)[nn_indices[j]].normal_z;
      float dist = nn_dists[j] * (2.0f - cos_angle * cos_angle);
 
      if (dist < min_dist) {
        min_dist = dist;
        min_index = static_cast<int>(j);
      }
    }
    if (min_dist > max_distance)
      continue;
 
    corr.index_query = idx_i;
    corr.index_match = nn_indices[min_index];
    corr.distance = nn_dists[min_index]; // min_dist;
    correspondences[nr_valid_correspondences++] = corr;
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}
 
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
void
CorrespondenceEstimationBackProjection<PointSource, PointTarget, NormalT, Scalar>::
    determineReciprocalCorrespondences(pcl::Correspondences& correspondences,
                                       double max_distance)
{
  if (!initCompute())
    return;
 
  // Set the internal point representation of choice
  if (!initComputeReciprocal())
    return;
 
  correspondences.resize(indices_->size());
 
  pcl::Indices nn_indices(k_);
  std::vector<float> nn_dists(k_);
  pcl::Indices index_reciprocal(1);
  std::vector<float> distance_reciprocal(1);
 
  int min_index = 0;
 
  pcl::Correspondence corr;
  unsigned int nr_valid_correspondences = 0;
  int target_idx = 0;
 
  // Iterate over the input set of source indices
  for (const auto& idx_i : (*indices_)) {
    // Check if the template types are the same. If true, avoid a copy.
    // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
    // macro!
    tree_->nearestKSearch(
        detail::pointCopyOrRef<PointTarget, PointSource>(input_, idx_i),
        k_,
        nn_indices,
        nn_dists);
 
    // Among the K nearest neighbours find the one with minimum perpendicular distance
    // to the normal
    float min_dist = std::numeric_limits<float>::max();
 
    // Find the best correspondence
    for (std::size_t j = 0; j < nn_indices.size(); j++) {
      float cos_angle = (*source_normals_)[idx_i].normal_x *
                            (*target_normals_)[nn_indices[j]].normal_x +
                        (*source_normals_)[idx_i].normal_y *
                            (*target_normals_)[nn_indices[j]].normal_y +
                        (*source_normals_)[idx_i].normal_z *
                            (*target_normals_)[nn_indices[j]].normal_z;
      float dist = nn_dists[j] * (2.0f - cos_angle * cos_angle);
 
      if (dist < min_dist) {
        min_dist = dist;
        min_index = static_cast<int>(j);
      }
    }
    if (min_dist > max_distance)
      continue;
 
    // Check if the correspondence is reciprocal
    target_idx = nn_indices[min_index];
    tree_reciprocal_->nearestKSearch(
        detail::pointCopyOrRef<PointSource, PointTarget>(target_, target_idx),
        1,
        index_reciprocal,
        distance_reciprocal);
 
    if (idx_i != index_reciprocal[0])
      continue;
 
    corr.index_query = idx_i;
    corr.index_match = nn_indices[min_index];
    corr.distance = nn_dists[min_index]; // min_dist;
    correspondences[nr_valid_correspondences++] = corr;
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}
 
} // namespace registration
} // namespace pcl
 
#endif // PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_