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rops_estimation.hpp
/*
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* Author : Sergey Ushakov
* Email : sergey.s.ushakov@mail.ru
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*/
#ifndef PCL_ROPS_ESTIMATION_HPP_
#define PCL_ROPS_ESTIMATION_HPP_
#include <pcl/features/rops_estimation.h>
#include <array>
#include <numeric> // for accumulate
#include <Eigen/Eigenvalues> // for EigenSolver
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT>
pcl::ROPSEstimation <PointInT, PointOutT>::ROPSEstimation () :
triangles_ (0),
triangles_of_the_point_ (0)
{
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT>
pcl::ROPSEstimation <PointInT, PointOutT>::~ROPSEstimation ()
{
triangles_.clear ();
triangles_of_the_point_.clear ();
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::setNumberOfPartitionBins (unsigned int number_of_bins)
{
if (number_of_bins != 0)
number_of_bins_ = number_of_bins;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> unsigned int
pcl::ROPSEstimation <PointInT, PointOutT>::getNumberOfPartitionBins () const
{
return (number_of_bins_);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::setNumberOfRotations (unsigned int number_of_rotations)
{
if (number_of_rotations != 0)
{
number_of_rotations_ = number_of_rotations;
step_ = 90.0f / static_cast <float> (number_of_rotations_ + 1);
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> unsigned int
pcl::ROPSEstimation <PointInT, PointOutT>::getNumberOfRotations () const
{
return (number_of_rotations_);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::setSupportRadius (float support_radius)
{
if (support_radius > 0.0f)
{
support_radius_ = support_radius;
sqr_support_radius_ = support_radius * support_radius;
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> float
pcl::ROPSEstimation <PointInT, PointOutT>::getSupportRadius () const
{
return (support_radius_);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::setTriangles (const std::vector <pcl::Vertices>& triangles)
{
triangles_ = triangles;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::getTriangles (std::vector <pcl::Vertices>& triangles) const
{
triangles = triangles_;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
if (triangles_.empty ())
{
output.clear ();
return;
}
buildListOfPointsTriangles ();
//feature size = number_of_rotations * number_of_axis_to_rotate_around * number_of_projections * number_of_central_moments
unsigned int feature_size = number_of_rotations_ * 3 * 3 * 5;
const auto number_of_points = indices_->size ();
output.clear ();
output.reserve (number_of_points);
for (const auto& idx: *indices_)
{
std::set <unsigned int> local_triangles;
pcl::Indices local_points;
getLocalSurface ((*input_)[idx], local_triangles, local_points);
Eigen::Matrix3f lrf_matrix;
computeLRF ((*input_)[idx], local_triangles, lrf_matrix);
PointCloudIn transformed_cloud;
transformCloud ((*input_)[idx], lrf_matrix, local_points, transformed_cloud);
std::array<PointInT, 3> axes;
axes[0].x = 1.0f; axes[0].y = 0.0f; axes[0].z = 0.0f;
axes[1].x = 0.0f; axes[1].y = 1.0f; axes[1].z = 0.0f;
axes[2].x = 0.0f; axes[2].y = 0.0f; axes[2].z = 1.0f;
std::vector <float> feature;
for (const auto &axis : axes)
{
float theta = step_;
do
{
//rotate local surface and get bounding box
PointCloudIn rotated_cloud;
Eigen::Vector3f min, max;
rotateCloud (axis, theta, transformed_cloud, rotated_cloud, min, max);
//for each projection (XY, XZ and YZ) compute distribution matrix and central moments
for (unsigned int i_proj = 0; i_proj < 3; i_proj++)
{
Eigen::MatrixXf distribution_matrix;
distribution_matrix.resize (number_of_bins_, number_of_bins_);
getDistributionMatrix (i_proj, min, max, rotated_cloud, distribution_matrix);
// TODO remove this needless copy due to API design
std::vector <float> moments;
computeCentralMoments (distribution_matrix, moments);
feature.insert (feature.end (), moments.begin (), moments.end ());
}
theta += step_;
} while (theta < 90.0f);
}
const float norm = std::accumulate(
feature.cbegin(), feature.cend(), 0.f, [](const auto& sum, const auto& val) {
return sum + std::abs(val);
});
float invert_norm;
if (norm < std::numeric_limits <float>::epsilon ())
invert_norm = 1.0f;
else
invert_norm = 1.0f / norm;
output.emplace_back ();
for (std::size_t i_dim = 0; i_dim < feature_size; i_dim++)
output.back().histogram[i_dim] = feature[i_dim] * invert_norm;
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::buildListOfPointsTriangles ()
{
triangles_of_the_point_.clear ();
std::vector <unsigned int> dummy;
dummy.reserve (100);
triangles_of_the_point_.resize (surface_->points. size (), dummy);
for (std::size_t i_triangle = 0; i_triangle < triangles_.size (); i_triangle++)
for (const auto& vertex: triangles_[i_triangle].vertices)
triangles_of_the_point_[vertex].push_back (i_triangle);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::getLocalSurface (const PointInT& point, std::set <unsigned int>& local_triangles, pcl::Indices& local_points) const
{
std::vector <float> distances;
tree_->radiusSearch (point, support_radius_, local_points, distances);
for (const auto& pt: local_points)
local_triangles.insert (triangles_of_the_point_[pt].begin (),
triangles_of_the_point_[pt].end ());
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::computeLRF (const PointInT& point, const std::set <unsigned int>& local_triangles, Eigen::Matrix3f& lrf_matrix) const
{
std::size_t number_of_triangles = local_triangles.size ();
std::vector<Eigen::Matrix3f, Eigen::aligned_allocator<Eigen::Matrix3f> > scatter_matrices;
std::vector <float> triangle_area (number_of_triangles), distance_weight (number_of_triangles);
scatter_matrices.reserve (number_of_triangles);
triangle_area.clear ();
distance_weight.clear ();
float total_area = 0.0f;
const float coeff = 1.0f / 12.0f;
const float coeff_1_div_3 = 1.0f / 3.0f;
Eigen::Vector3f feature_point (point.x, point.y, point.z);
for (const auto& triangle: local_triangles)
{
Eigen::Vector3f pt[3];
for (unsigned int i_vertex = 0; i_vertex < 3; i_vertex++)
{
const unsigned int index = triangles_[triangle].vertices[i_vertex];
pt[i_vertex] (0) = (*surface_)[index].x;
pt[i_vertex] (1) = (*surface_)[index].y;
pt[i_vertex] (2) = (*surface_)[index].z;
}
const float curr_area = ((pt[1] - pt[0]).cross (pt[2] - pt[0])).norm ();
triangle_area.push_back (curr_area);
total_area += curr_area;
distance_weight.push_back (std::pow (support_radius_ - (feature_point - (pt[0] + pt[1] + pt[2]) * coeff_1_div_3).norm (), 2.0f));
Eigen::Matrix3f curr_scatter_matrix;
curr_scatter_matrix.setZero ();
for (const auto &i_pt : pt)
{
Eigen::Vector3f vec = i_pt - feature_point;
curr_scatter_matrix += vec * (vec.transpose ());
for (const auto &j_pt : pt)
curr_scatter_matrix += vec * ((j_pt - feature_point).transpose ());
}
scatter_matrices.emplace_back (coeff * curr_scatter_matrix);
}
if (std::abs (total_area) < std::numeric_limits <float>::epsilon ())
total_area = 1.0f / total_area;
else
total_area = 1.0f;
Eigen::Matrix3f overall_scatter_matrix;
overall_scatter_matrix.setZero ();
std::vector<float> total_weight (number_of_triangles);
const float denominator = 1.0f / 6.0f;
for (std::size_t i_triangle = 0; i_triangle < number_of_triangles; i_triangle++)
{
const float factor = distance_weight[i_triangle] * triangle_area[i_triangle] * total_area;
overall_scatter_matrix += factor * scatter_matrices[i_triangle];
total_weight[i_triangle] = factor * denominator;
}
Eigen::Vector3f v1, v2, v3;
computeEigenVectors (overall_scatter_matrix, v1, v2, v3);
float h1 = 0.0f;
float h3 = 0.0f;
std::size_t i_triangle = 0;
for (const auto& triangle: local_triangles)
{
Eigen::Vector3f pt[3];
for (unsigned int i_vertex = 0; i_vertex < 3; i_vertex++)
{
const unsigned int index = triangles_[triangle].vertices[i_vertex];
pt[i_vertex] (0) = (*surface_)[index].x;
pt[i_vertex] (1) = (*surface_)[index].y;
pt[i_vertex] (2) = (*surface_)[index].z;
}
float factor1 = 0.0f;
float factor3 = 0.0f;
for (const auto &i_pt : pt)
{
Eigen::Vector3f vec = i_pt - feature_point;
factor1 += vec.dot (v1);
factor3 += vec.dot (v3);
}
h1 += total_weight[i_triangle] * factor1;
h3 += total_weight[i_triangle] * factor3;
i_triangle++;
}
if (h1 < 0.0f) v1 = -v1;
if (h3 < 0.0f) v3 = -v3;
v2 = v3.cross (v1);
lrf_matrix.row (0) = v1;
lrf_matrix.row (1) = v2;
lrf_matrix.row (2) = v3;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::computeEigenVectors (const Eigen::Matrix3f& matrix,
Eigen::Vector3f& major_axis, Eigen::Vector3f& middle_axis, Eigen::Vector3f& minor_axis) const
{
Eigen::EigenSolver <Eigen::Matrix3f> eigen_solver;
eigen_solver.compute (matrix);
Eigen::EigenSolver <Eigen::Matrix3f>::EigenvectorsType eigen_vectors;
Eigen::EigenSolver <Eigen::Matrix3f>::EigenvalueType eigen_values;
eigen_vectors = eigen_solver.eigenvectors ();
eigen_values = eigen_solver.eigenvalues ();
unsigned int temp = 0;
unsigned int major_index = 0;
unsigned int middle_index = 1;
unsigned int minor_index = 2;
if (eigen_values.real () (major_index) < eigen_values.real () (middle_index))
{
temp = major_index;
major_index = middle_index;
middle_index = temp;
}
if (eigen_values.real () (major_index) < eigen_values.real () (minor_index))
{
temp = major_index;
major_index = minor_index;
minor_index = temp;
}
if (eigen_values.real () (middle_index) < eigen_values.real () (minor_index))
{
temp = minor_index;
minor_index = middle_index;
middle_index = temp;
}
major_axis = eigen_vectors.col (major_index).real ();
middle_axis = eigen_vectors.col (middle_index).real ();
minor_axis = eigen_vectors.col (minor_index).real ();
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::transformCloud (const PointInT& point, const Eigen::Matrix3f& matrix, const pcl::Indices& local_points, PointCloudIn& transformed_cloud) const
{
const auto number_of_points = local_points.size ();
transformed_cloud.clear ();
transformed_cloud.reserve (number_of_points);
for (const auto& idx: local_points)
{
Eigen::Vector3f transformed_point ((*surface_)[idx].x - point.x,
(*surface_)[idx].y - point.y,
(*surface_)[idx].z - point.z);
transformed_point = matrix * transformed_point;
PointInT new_point;
new_point.x = transformed_point (0);
new_point.y = transformed_point (1);
new_point.z = transformed_point (2);
transformed_cloud.emplace_back (new_point);
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::rotateCloud (const PointInT& axis, const float angle, const PointCloudIn& cloud, PointCloudIn& rotated_cloud, Eigen::Vector3f& min, Eigen::Vector3f& max) const
{
Eigen::Matrix3f rotation_matrix;
const float x = axis.x;
const float y = axis.y;
const float z = axis.z;
const float rad = M_PI / 180.0f;
const float cosine = std::cos (angle * rad);
const float sine = std::sin (angle * rad);
rotation_matrix << cosine + (1 - cosine) * x * x, (1 - cosine) * x * y - sine * z, (1 - cosine) * x * z + sine * y,
(1 - cosine) * y * x + sine * z, cosine + (1 - cosine) * y * y, (1 - cosine) * y * z - sine * x,
(1 - cosine) * z * x - sine * y, (1 - cosine) * z * y + sine * x, cosine + (1 - cosine) * z * z;
const auto number_of_points = cloud.size ();
rotated_cloud.header = cloud.header;
rotated_cloud.width = number_of_points;
rotated_cloud.height = 1;
rotated_cloud.clear ();
rotated_cloud.reserve (number_of_points);
min (0) = std::numeric_limits <float>::max ();
min (1) = std::numeric_limits <float>::max ();
min (2) = std::numeric_limits <float>::max ();
max (0) = -std::numeric_limits <float>::max ();
max (1) = -std::numeric_limits <float>::max ();
max (2) = -std::numeric_limits <float>::max ();
for (const auto& pt: cloud.points)
{
Eigen::Vector3f point (pt.x, pt.y, pt.z);
point = rotation_matrix * point;
PointInT rotated_point;
rotated_point.x = point (0);
rotated_point.y = point (1);
rotated_point.z = point (2);
rotated_cloud.emplace_back (rotated_point);
for (int i = 0; i < 3; ++i)
{
min(i) = std::min(min(i), point(i));
max(i) = std::max(max(i), point(i));
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::getDistributionMatrix (const unsigned int projection, const Eigen::Vector3f& min, const Eigen::Vector3f& max, const PointCloudIn& cloud, Eigen::MatrixXf& matrix) const
{
matrix.setZero ();
const unsigned int coord[3][2] = {
{0, 1},
{0, 2},
{1, 2}};
const float u_bin_length = (max (coord[projection][0]) - min (coord[projection][0])) / number_of_bins_;
const float v_bin_length = (max (coord[projection][1]) - min (coord[projection][1])) / number_of_bins_;
for (const auto& pt: cloud.points)
{
Eigen::Vector3f point (pt.x, pt.y, pt.z);
const float u_length = point (coord[projection][0]) - min[coord[projection][0]];
const float v_length = point (coord[projection][1]) - min[coord[projection][1]];
const float u_ratio = u_length / u_bin_length;
auto row = static_cast <unsigned int> (u_ratio);
if (row == number_of_bins_) row--;
const float v_ratio = v_length / v_bin_length;
auto col = static_cast <unsigned int> (v_ratio);
if (col == number_of_bins_) col--;
matrix (row, col) += 1.0f;
}
matrix /= std::max<float> (1, cloud.size ());
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::ROPSEstimation <PointInT, PointOutT>::computeCentralMoments (const Eigen::MatrixXf& matrix, std::vector <float>& moments) const
{
float mean_i = 0.0f;
float mean_j = 0.0f;
for (unsigned int i = 0; i < number_of_bins_; i++)
for (unsigned int j = 0; j < number_of_bins_; j++)
{
const float m = matrix (i, j);
mean_i += static_cast <float> (i + 1) * m;
mean_j += static_cast <float> (j + 1) * m;
}
const unsigned int number_of_moments_to_compute = 4;
const float power[number_of_moments_to_compute][2] = {
{1.0f, 1.0f},
{2.0f, 1.0f},
{1.0f, 2.0f},
{2.0f, 2.0f}};
float entropy = 0.0f;
moments.resize (number_of_moments_to_compute + 1, 0.0f);
for (unsigned int i = 0; i < number_of_bins_; i++)
{
const float i_factor = static_cast <float> (i + 1) - mean_i;
for (unsigned int j = 0; j < number_of_bins_; j++)
{
const float j_factor = static_cast <float> (j + 1) - mean_j;
const float m = matrix (i, j);
if (m > 0.0f)
entropy -= m * std::log (m);
for (unsigned int i_moment = 0; i_moment < number_of_moments_to_compute; i_moment++)
moments[i_moment] += std::pow (i_factor, power[i_moment][0]) * std::pow (j_factor, power[i_moment][1]) * m;
}
}
moments[number_of_moments_to_compute] = entropy;
}
#endif // PCL_ROPS_ESTIMATION_HPP_
pcl::ROPSEstimation::getSupportRadius
float getSupportRadius() const
Returns the support radius.
Definition rops_estimation.hpp:112
pcl::ROPSEstimation::setNumberOfRotations
void setNumberOfRotations(unsigned int number_of_rotations)
This method sets the number of rotations.
Definition rops_estimation.hpp:83
pcl::ROPSEstimation::ROPSEstimation
ROPSEstimation()
Simple constructor.
Definition rops_estimation.hpp:51
pcl::ROPSEstimation::setNumberOfPartitionBins
void setNumberOfPartitionBins(unsigned int number_of_bins)
Allows to set the number of partition bins that is used for distribution matrix calculation.
Definition rops_estimation.hpp:68
pcl::ROPSEstimation::getNumberOfPartitionBins
unsigned int getNumberOfPartitionBins() const
Returns the number of partition bins.
Definition rops_estimation.hpp:76
pcl::ROPSEstimation::getNumberOfRotations
unsigned int getNumberOfRotations() const
returns the number of rotations.
Definition rops_estimation.hpp:94
pcl::ROPSEstimation::setSupportRadius
void setSupportRadius(float support_radius)
Allows to set the support radius that is used to crop the local surface of the point.
Definition rops_estimation.hpp:101
pcl::ROPSEstimation::setTriangles
void setTriangles(const std::vector< pcl::Vertices > &triangles)
This method sets the triangles of the mesh.
Definition rops_estimation.hpp:119
pcl::ROPSEstimation::getTriangles
void getTriangles(std::vector< pcl::Vertices > &triangles) const
Returns the triangles of the mesh.
Definition rops_estimation.hpp:126
pcl::device::norm
__device__ __host__ __forceinline__ float norm(const float3 &v1, const float3 &v2)
Definition vector_operations.hpp:60
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
M_PI
#define M_PI
Definition pcl_macros.h:201