template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
// Allocate enough space to hold the results
// \note This resize is irrelevant for a radiusSearch ().
std::vector<int> nn_indices (k_);
std::vector<float> nn_dists (k_);
output.is_dense = true;
// Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
if (input_->is_dense)
{
// Iterating over the entire index vector
for (size_t idx = 0; idx < indices_->size (); ++idx)
{
if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
{
output.points[idx].principal_curvature[0] = output.points[idx].principal_curvature[1] = output.points[idx].principal_curvature[2] =
output.points[idx].pc1 = output.points[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// Estimate the principal curvatures at each patch
computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
output.points[idx].principal_curvature[0], output.points[idx].principal_curvature[1], output.points[idx].principal_curvature[2],
output.points[idx].pc1, output.points[idx].pc2);
}
}
else
{
// Iterating over the entire index vector
for (size_t idx = 0; idx < indices_->size (); ++idx)
{
if (!isFinite ((*input_)[(*indices_)[idx]]) ||
this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
{
output.points[idx].principal_curvature[0] = output.points[idx].principal_curvature[1] = output.points[idx].principal_curvature[2] =
output.points[idx].pc1 = output.points[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// Estimate the principal curvatures at each patch
computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
output.points[idx].principal_curvature[0], output.points[idx].principal_curvature[1], output.points[idx].principal_curvature[2],
output.points[idx].pc1, output.points[idx].pc2);
}
}
}
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
// Check if input was set
if (!normals_)
{
ROS_ERROR ("[pcl::%s::computeFeature] No input dataset containing normals was given!", getClassName ().c_str ());
output.width = output.height = 0;
output.points.clear ();
return;
}
if (normals_->points.size () != surface_->points.size ())
{
ROS_ERROR ("[pcl::%s::computeFeature] The number of points in the input dataset (%zu) differs from the number of points in the dataset containing the normals (%zu)!",
getClassName ().c_str (), surface_->points.size (), normals_->points.size ());
output.width = output.height = 0;
output.points.clear ();
return;
}
// Allocate enough space to hold the results
// \note This resize is irrelevant for a radiusSearch ().
std::vector<int> nn_indices (k_);
std::vector<float> nn_dists (k_);
// Iterating over the entire index vector
for (size_t idx = 0; idx < indices_->size (); ++idx)
{
searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists);
// Estimate the principal curvatures at each patch
computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
output.points[idx].principal_curvature[0], output.points[idx].principal_curvature[1], output.points[idx].principal_curvature[2],
output.points[idx].pc1, output.points[idx].pc2);
}
}