Point Cloud Library (PCL) 1.13.0
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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//////////////////////////////////////////////////////////////////////////////////////////////
49template <typename PointNT> void
51{
52 // Initialize data structures
53 const auto N = static_cast<unsigned int> (input_->size ());
54 Eigen::MatrixXd M (2*N, 2*N),
55 d (2*N, 1);
56
57 for (unsigned int row_i = 0; row_i < 2*N; ++row_i)
58 {
59 // boolean variable to determine whether we are in the off_surface domain for the rows
60 bool row_off = (row_i >= N);
61 for (unsigned int col_i = 0; col_i < 2*N; ++col_i)
62 {
63 // boolean variable to determine whether we are in the off_surface domain for the columns
64 bool col_off = (col_i >= N);
65 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_,
66 Eigen::Vector3f ((*input_)[row_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f ((*input_)[row_i%N].getNormalVector3fMap ()).cast<double> () * row_off * off_surface_epsilon_);
67 }
68
69 d (row_i, 0) = row_off * off_surface_epsilon_;
70 }
71
72 // Solve for the weights
73 Eigen::MatrixXd w (2*N, 1);
74
75 // Solve_linear_system (M, d, w);
76 w = M.fullPivLu ().solve (d);
77
78 std::vector<double> weights (2*N);
79 std::vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d> > centers (2*N);
80 for (unsigned int i = 0; i < N; ++i)
81 {
82 centers[i] = Eigen::Vector3f ((*input_)[i].getVector3fMap ()).cast<double> ();
83 centers[i + N] = Eigen::Vector3f ((*input_)[i].getVector3fMap ()).cast<double> () + Eigen::Vector3f ((*input_)[i].getNormalVector3fMap ()).cast<double> () * off_surface_epsilon_;
84 weights[i] = w (i, 0);
85 weights[i + N] = w (i + N, 0);
86 }
87
88 for (int x = 0; x < res_x_; ++x)
89 for (int y = 0; y < res_y_; ++y)
90 for (int z = 0; z < res_z_; ++z)
91 {
92 const Eigen::Vector3f point_f = (size_voxel_ * Eigen::Array3f (x, y, z)
93 + lower_boundary_).matrix ();
94 const Eigen::Vector3d point = point_f.cast<double> ();
95
96 double f = 0.0;
97 std::vector<double>::const_iterator w_it (weights.begin());
98 for (std::vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d> >::const_iterator c_it = centers.begin ();
99 c_it != centers.end (); ++c_it, ++w_it)
100 f += *w_it * kernel (*c_it, point);
101
102 grid_[x * res_y_*res_z_ + y * res_z_ + z] = float (f);
103 }
104}
105
106//////////////////////////////////////////////////////////////////////////////////////////////
107template <typename PointNT> double
108pcl::MarchingCubesRBF<PointNT>::kernel (Eigen::Vector3d c, Eigen::Vector3d x)
109{
110 double r = (x - c).norm ();
111 return (r * r * r);
112}
113
114#define PCL_INSTANTIATE_MarchingCubesRBF(T) template class PCL_EXPORTS pcl::MarchingCubesRBF<T>;
115
116#endif // PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
117
void voxelizeData() override
Convert the point cloud into voxel data.
double kernel(Eigen::Vector3d c, Eigen::Vector3d x)
the Radial Basis Function kernel.
~MarchingCubesRBF() override
Destructor.