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>
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar> bool
pcl::registration::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
pcl::registration::CorrespondenceEstimationBackProjection<PointSource, PointTarget, NormalT, Scalar>::determineCorrespondences (
pcl::Correspondences &correspondences, double max_distance)
{
if (!initCompute ())
return;
correspondences.resize (indices_->size ());
std::vector<int> nn_indices (k_);
std::vector<float> nn_dists (k_);
float min_dist = std::numeric_limits<float>::max ();
int min_index = 0;
pcl::Correspondence corr;
unsigned int nr_valid_correspondences = 0;
// 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!
if (isSamePointType<PointSource, PointTarget> ())
{
PointTarget pt;
// Iterate over the input set of source indices
for (std::vector<int>::const_iterator idx_i = indices_->begin (); idx_i != indices_->end (); ++idx_i)
{
tree_->nearestKSearch (input_->points[*idx_i], k_, nn_indices, nn_dists);
// Among the K nearest neighbours find the one with minimum perpendicular distance to the normal
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_->points[*idx_i].normal_x * target_normals_->points[nn_indices[j]].normal_x +
source_normals_->points[*idx_i].normal_y * target_normals_->points[nn_indices[j]].normal_y +
source_normals_->points[*idx_i].normal_z * target_normals_->points[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;
}
}
else
{
PointTarget pt;
// Iterate over the input set of source indices
for (std::vector<int>::const_iterator idx_i = indices_->begin (); idx_i != indices_->end (); ++idx_i)
{
tree_->nearestKSearch (input_->points[*idx_i], k_, nn_indices, nn_dists);
// Among the K nearest neighbours find the one with minimum perpendicular distance to the normal
min_dist = std::numeric_limits<float>::max ();
// Find the best correspondence
for (std::size_t j = 0; j < nn_indices.size (); j++)
{
PointSource pt_src;
// Copy the source data to a target PointTarget format so we can search in the tree
copyPoint (input_->points[*idx_i], pt_src);
float cos_angle = source_normals_->points[*idx_i].normal_x * target_normals_->points[nn_indices[j]].normal_x +
source_normals_->points[*idx_i].normal_y * target_normals_->points[nn_indices[j]].normal_y +
source_normals_->points[*idx_i].normal_z * target_normals_->points[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
pcl::registration::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 ());
std::vector<int> nn_indices (k_);
std::vector<float> nn_dists (k_);
std::vector<int> index_reciprocal (1);
std::vector<float> distance_reciprocal (1);
float min_dist = std::numeric_limits<float>::max ();
int min_index = 0;
pcl::Correspondence corr;
unsigned int nr_valid_correspondences = 0;
int target_idx = 0;
// 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!
if (isSamePointType<PointSource, PointTarget> ())
{
PointTarget pt;
// Iterate over the input set of source indices
for (std::vector<int>::const_iterator idx_i = indices_->begin (); idx_i != indices_->end (); ++idx_i)
{
tree_->nearestKSearch (input_->points[*idx_i], k_, nn_indices, nn_dists);
// Among the K nearest neighbours find the one with minimum perpendicular distance to the normal
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_->points[*idx_i].normal_x * target_normals_->points[nn_indices[j]].normal_x +
source_normals_->points[*idx_i].normal_y * target_normals_->points[nn_indices[j]].normal_y +
source_normals_->points[*idx_i].normal_z * target_normals_->points[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 (target_->points[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;
}
}
else
{
PointTarget pt;
// Iterate over the input set of source indices
for (std::vector<int>::const_iterator idx_i = indices_->begin (); idx_i != indices_->end (); ++idx_i)
{
tree_->nearestKSearch (input_->points[*idx_i], k_, nn_indices, nn_dists);
// Among the K nearest neighbours find the one with minimum perpendicular distance to the normal
min_dist = std::numeric_limits<float>::max ();
// Find the best correspondence
for (std::size_t j = 0; j < nn_indices.size (); j++)
{
PointSource pt_src;
// Copy the source data to a target PointTarget format so we can search in the tree
copyPoint (input_->points[*idx_i], pt_src);
float cos_angle = source_normals_->points[*idx_i].normal_x * target_normals_->points[nn_indices[j]].normal_x +
source_normals_->points[*idx_i].normal_y * target_normals_->points[nn_indices[j]].normal_y +
source_normals_->points[*idx_i].normal_z * target_normals_->points[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 (target_->points[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 ();
}
#endif // PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_
pcl::Correspondence::index_match
int index_match
Index of the matching (target) point.
Definition: correspondence.h:64
pcl::Correspondence::distance
float distance
Definition: correspondence.h:68
pcl::registration::CorrespondenceEstimationBase
Abstract CorrespondenceEstimationBase class.
Definition: correspondence_estimation.h:62
pcl::copyPoint
void copyPoint(const PointInT &point_in, PointOutT &point_out)
Copy the fields of a source point into a target point.
Definition: copy_point.hpp:138
pcl::registration::CorrespondenceEstimationBackProjection::determineReciprocalCorrespondences
virtual void determineReciprocalCorrespondences(pcl::Correspondences &correspondences, double max_distance=std::numeric_limits< double >::max())
Determine the reciprocal correspondences between input and target cloud.
Definition: correspondence_estimation_backprojection.hpp:157
pcl::Correspondence::index_query
int index_query
Index of the query (source) point.
Definition: correspondence.h:62
pcl::registration::CorrespondenceEstimationBackProjection::determineCorrespondences
void determineCorrespondences(pcl::Correspondences &correspondences, double max_distance=std::numeric_limits< double >::max())
Determine the correspondences between input and target cloud.
Definition: correspondence_estimation_backprojection.hpp:59
pcl::Correspondences
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
Definition: correspondence.h:88
pcl::Correspondence
Correspondence represents a match between two entities (e.g., points, descriptors,...
Definition: correspondence.h:59
pcl::registration::CorrespondenceEstimationBackProjection::initCompute
bool initCompute()
Internal computation initialization.
Definition: correspondence_estimation_backprojection.hpp:46