Point Cloud Library (PCL) 1.12.0
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marching_cubes_rbf.hpp
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38
39#ifndef PCL_SURFACE_IMPL_MARCHING_CUBES_RBF_H_
40#define PCL_SURFACE_IMPL_MARCHING_CUBES_RBF_H_
41
42#include <pcl/surface/marching_cubes_rbf.h>
43
44//////////////////////////////////////////////////////////////////////////////////////////////
45template <typename PointNT>
47{
48}
49
50//////////////////////////////////////////////////////////////////////////////////////////////
51template <typename PointNT> void
53{
54 // Initialize data structures
55 const unsigned int N = static_cast<unsigned int> (input_->size ());
56 Eigen::MatrixXd M (2*N, 2*N),
57 d (2*N, 1);
58
59 for (unsigned int row_i = 0; row_i < 2*N; ++row_i)
60 {
61 // boolean variable to determine whether we are in the off_surface domain for the rows
62 bool row_off = (row_i >= N);
63 for (unsigned int col_i = 0; col_i < 2*N; ++col_i)
64 {
65 // boolean variable to determine whether we are in the off_surface domain for the columns
66 bool col_off = (col_i >= N);
67 M (row_i, col_i) = kernel (Eigen::Vector3f ((*input_)[col_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f ((*input_)[col_i%N].getNormalVector3fMap ()).cast<double> () * col_off * off_surface_epsilon_,
68 Eigen::Vector3f ((*input_)[row_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f ((*input_)[row_i%N].getNormalVector3fMap ()).cast<double> () * row_off * off_surface_epsilon_);
69 }
70
71 d (row_i, 0) = row_off * off_surface_epsilon_;
72 }
73
74 // Solve for the weights
75 Eigen::MatrixXd w (2*N, 1);
76
77 // Solve_linear_system (M, d, w);
78 w = M.fullPivLu ().solve (d);
79
80 std::vector<double> weights (2*N);
81 std::vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d> > centers (2*N);
82 for (unsigned int i = 0; i < N; ++i)
83 {
84 centers[i] = Eigen::Vector3f ((*input_)[i].getVector3fMap ()).cast<double> ();
85 centers[i + N] = Eigen::Vector3f ((*input_)[i].getVector3fMap ()).cast<double> () + Eigen::Vector3f ((*input_)[i].getNormalVector3fMap ()).cast<double> () * off_surface_epsilon_;
86 weights[i] = w (i, 0);
87 weights[i + N] = w (i + N, 0);
88 }
89
90 for (int x = 0; x < res_x_; ++x)
91 for (int y = 0; y < res_y_; ++y)
92 for (int z = 0; z < res_z_; ++z)
93 {
94 const Eigen::Vector3f point_f = (size_voxel_ * Eigen::Array3f (x, y, z)
95 + lower_boundary_).matrix ();
96 const Eigen::Vector3d point = point_f.cast<double> ();
97
98 double f = 0.0;
99 std::vector<double>::const_iterator w_it (weights.begin());
100 for (std::vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d> >::const_iterator c_it = centers.begin ();
101 c_it != centers.end (); ++c_it, ++w_it)
102 f += *w_it * kernel (*c_it, point);
103
104 grid_[x * res_y_*res_z_ + y * res_z_ + z] = float (f);
105 }
106}
107
108//////////////////////////////////////////////////////////////////////////////////////////////
109template <typename PointNT> double
110pcl::MarchingCubesRBF<PointNT>::kernel (Eigen::Vector3d c, Eigen::Vector3d x)
111{
112 double r = (x - c).norm ();
113 return (r * r * r);
114}
115
116#define PCL_INSTANTIATE_MarchingCubesRBF(T) template class PCL_EXPORTS pcl::MarchingCubesRBF<T>;
117
118#endif // PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
119
void voxelizeData() override
Convert the point cloud into voxel data.
double kernel(Eigen::Vector3d c, Eigen::Vector3d x)
the Radial Basis Function kernel.