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normal_based_signature.hpp
/*
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* Point Cloud Library (PCL) - www.pointclouds.org
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#ifndef PCL_FEATURES_IMPL_NORMAL_BASED_SIGNATURE_H_
#define PCL_FEATURES_IMPL_NORMAL_BASED_SIGNATURE_H_
#include <pcl/features/normal_based_signature.h>
template <typename PointT, typename PointNT, typename PointFeature> void
pcl::NormalBasedSignatureEstimation<PointT, PointNT, PointFeature>::computeFeature (FeatureCloud &output)
{
// do a few checks before starting the computations
PointFeature test_feature;
if (N_prime_ * M_prime_ != sizeof (test_feature.values) / sizeof (float))
{
PCL_ERROR ("NormalBasedSignatureEstimation: not using the proper signature size: %u vs %u\n", N_prime_ * M_prime_, sizeof (test_feature.values) / sizeof (float));
return;
}
pcl::Indices k_indices;
std::vector<float> k_sqr_distances;
tree_->setInputCloud (input_);
output.resize (indices_->size ());
for (std::size_t index_i = 0; index_i < indices_->size (); ++index_i)
{
std::size_t point_i = (*indices_)[index_i];
Eigen::MatrixXf s_matrix (N_, M_);
Eigen::Vector4f center_point = (*input_)[point_i].getVector4fMap ();
for (std::size_t k = 0; k < N_; ++k)
{
Eigen::VectorXf s_row (M_);
for (std::size_t l = 0; l < M_; ++l)
{
Eigen::Vector4f normal = (*normals_)[point_i].getNormalVector4fMap ();
Eigen::Vector4f normal_u = Eigen::Vector4f::Zero ();
Eigen::Vector4f normal_v = Eigen::Vector4f::Zero ();
if (std::abs (normal.x ()) > 0.0001f)
{
normal_u.x () = - normal.y () / normal.x ();
normal_u.y () = 1.0f;
normal_u.z () = 0.0f;
normal_u.normalize ();
}
else if (std::abs (normal.y ()) > 0.0001f)
{
normal_u.x () = 1.0f;
normal_u.y () = - normal.x () / normal.y ();
normal_u.z () = 0.0f;
normal_u.normalize ();
}
else
{
normal_u.x () = 0.0f;
normal_u.y () = 1.0f;
normal_u.z () = - normal.y () / normal.z ();
}
normal_v = normal.cross3 (normal_u);
Eigen::Vector4f zeta_point = 2.0f * static_cast<float> (l + 1) * scale_h_ / static_cast<float> (M_) *
(std::cos (2.0f * static_cast<float> (M_PI) * static_cast<float> ((k + 1) / N_)) * normal_u +
sinf (2.0f * static_cast<float> (M_PI) * static_cast<float> ((k + 1) / N_)) * normal_v);
// Compute normal by using the neighbors
Eigen::Vector4f zeta_point_plus_center = zeta_point + center_point;
PointT zeta_point_pcl;
zeta_point_pcl.x = zeta_point_plus_center.x (); zeta_point_pcl.y = zeta_point_plus_center.y (); zeta_point_pcl.z = zeta_point_plus_center.z ();
tree_->radiusSearch (zeta_point_pcl, search_radius_, k_indices, k_sqr_distances);
// Do k nearest search if there are no neighbors nearby
if (k_indices.empty ())
{
k_indices.resize (5);
k_sqr_distances.resize (5);
tree_->nearestKSearch (zeta_point_pcl, 5, k_indices, k_sqr_distances);
}
Eigen::Vector4f average_normal = Eigen::Vector4f::Zero ();
float average_normalization_factor = 0.0f;
// Normals weighted by 1/squared_distances
for (std::size_t nn_i = 0; nn_i < k_indices.size (); ++nn_i)
{
if (k_sqr_distances[nn_i] < 1e-7f)
{
average_normal = (*normals_)[k_indices[nn_i]].getNormalVector4fMap ();
average_normalization_factor = 1.0f;
break;
}
average_normal += (*normals_)[k_indices[nn_i]].getNormalVector4fMap () / k_sqr_distances[nn_i];
average_normalization_factor += 1.0f / k_sqr_distances[nn_i];
}
average_normal /= average_normalization_factor;
float s = zeta_point.dot (average_normal) / zeta_point.norm ();
s_row[l] = s;
}
// do DCT on the s_matrix row-wise
Eigen::VectorXf dct_row (M_);
for (Eigen::Index m = 0; m < s_row.size (); ++m)
{
float Xk = 0.0f;
for (Eigen::Index n = 0; n < s_row.size (); ++n)
Xk += static_cast<float> (s_row[n] * std::cos (M_PI / (static_cast<double> (M_ * n) + 0.5) * static_cast<double> (k)));
dct_row[m] = Xk;
}
s_row = dct_row;
s_matrix.row (k).matrix () = dct_row;
}
// do DFT on the s_matrix column-wise
Eigen::MatrixXf dft_matrix (N_, M_);
for (std::size_t column_i = 0; column_i < M_; ++column_i)
{
Eigen::VectorXf dft_col (N_);
for (std::size_t k = 0; k < N_; ++k)
{
float Xk_real = 0.0f, Xk_imag = 0.0f;
for (std::size_t n = 0; n < N_; ++n)
{
Xk_real += static_cast<float> (s_matrix (n, column_i) * std::cos (2.0f * M_PI / static_cast<double> (N_ * k * n)));
Xk_imag += static_cast<float> (s_matrix (n, column_i) * sin (2.0f * M_PI / static_cast<double> (N_ * k * n)));
}
dft_col[k] = std::sqrt (Xk_real*Xk_real + Xk_imag*Xk_imag);
}
dft_matrix.col (column_i).matrix () = dft_col;
}
Eigen::MatrixXf final_matrix = dft_matrix.block (0, 0, N_prime_, M_prime_);
PointFeature feature_point;
for (std::size_t i = 0; i < N_prime_; ++i)
for (std::size_t j = 0; j < M_prime_; ++j)
feature_point.values[i*M_prime_ + j] = final_matrix (i, j);
output[index_i] = feature_point;
}
}
#define PCL_INSTANTIATE_NormalBasedSignatureEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::NormalBasedSignatureEstimation<T,NT,OutT>;
#endif /* PCL_FEATURES_IMPL_NORMAL_BASED_SIGNATURE_H_ */
pcl::NormalBasedSignatureEstimation::computeFeature
void computeFeature(FeatureCloud &output) override
Definition normal_based_signature.hpp:46
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition point_cloud.h:462
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
M_PI
#define M_PI
Definition pcl_macros.h:201
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition point_types.hpp:623