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;
typedef typename pcl::traits::fieldList<PointTarget>::type FieldListTarget;
// 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 (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 (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
pcl::for_each_type <FieldListTarget> (pcl::NdConcatenateFunctor <PointSource, PointTarget> (
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 ();
}
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar> void
pcl::registration::CorrespondenceEstimationNormalShooting<PointSource, PointTarget, NormalT, Scalar>::determineReciprocalCorrespondences (
pcl::Correspondences &correspondences, double max_distance)
{
if (!initCompute ())
return;
typedef typename pcl::traits::fieldList<PointSource>::type FieldListSource;
typedef typename pcl::traits::fieldList<PointTarget>::type FieldListTarget;
typedef typename pcl::intersect<FieldListSource, FieldListTarget>::type FieldList;
// setup tree for reciprocal search
// 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);
double min_dist = std::numeric_limits<double>::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<double>::max ();
// Find the best correspondence
for (size_t j = 0; j < nn_indices.size (); j++)
{
// computing the distance between a point and a line in 3d.
// Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
pt.x = target_->points[nn_indices[j]].x - input_->points[*idx_i].x;
pt.y = target_->points[nn_indices[j]].y - input_->points[*idx_i].y;
pt.z = target_->points[nn_indices[j]].z - input_->points[*idx_i].z;
const NormalT &normal = source_normals_->points[*idx_i];
Eigen::Vector3d N (normal.normal_x, normal.normal_y, normal.normal_z);
Eigen::Vector3d V (pt.x, pt.y, pt.z);
Eigen::Vector3d C = N.cross (V);
// Check if we have a better correspondence
double dist = C.dot (C);
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;
// Correspondence IS reciprocal, save it and 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<double>::max ();
// Find the best correspondence
for (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
pcl::for_each_type <FieldListTarget> (pcl::NdConcatenateFunctor <PointSource, PointTarget> (
input_->points[*idx_i],
pt_src));
// computing the distance between a point and a line in 3d.
// Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
pt.x = target_->points[nn_indices[j]].x - pt_src.x;
pt.y = target_->points[nn_indices[j]].y - pt_src.y;
pt.z = target_->points[nn_indices[j]].z - pt_src.z;
const NormalT &normal = source_normals_->points[*idx_i];
Eigen::Vector3d N (normal.normal_x, normal.normal_y, normal.normal_z);
Eigen::Vector3d V (pt.x, pt.y, pt.z);
Eigen::Vector3d C = N.cross (V);
// Check if we have a better correspondence
double dist = C.dot (C);
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;
// Correspondence IS reciprocal, save it and 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 ();
}
