Point Cloud Library (PCL)  1.7.1
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 #include <pcl/common/common.h>
44 #include <pcl/common/vector_average.h>
45 #include <pcl/Vertices.h>
46 #include <pcl/kdtree/kdtree_flann.h>
47 
48 //////////////////////////////////////////////////////////////////////////////////////////////
49 template <typename PointNT>
51  : MarchingCubes<PointNT> (),
52  off_surface_epsilon_ (0.1f)
53 {
54 }
55 
56 //////////////////////////////////////////////////////////////////////////////////////////////
57 template <typename PointNT>
59 {
60 }
61 
62 //////////////////////////////////////////////////////////////////////////////////////////////
63 template <typename PointNT> void
65 {
66  // Initialize data structures
67  unsigned int N = static_cast<unsigned int> (input_->size ());
68  Eigen::MatrixXd M (2*N, 2*N),
69  d (2*N, 1);
70 
71  for (unsigned int row_i = 0; row_i < 2*N; ++row_i)
72  {
73  // boolean variable to determine whether we are in the off_surface domain for the rows
74  bool row_off = (row_i >= N) ? 1 : 0;
75  for (unsigned int col_i = 0; col_i < 2*N; ++col_i)
76  {
77  // boolean variable to determine whether we are in the off_surface domain for the columns
78  bool col_off = (col_i >= N) ? 1 : 0;
79  M (row_i, col_i) = kernel (Eigen::Vector3f (input_->points[col_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[col_i%N].getNormalVector3fMap ()).cast<double> () * col_off * off_surface_epsilon_,
80  Eigen::Vector3f (input_->points[row_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[row_i%N].getNormalVector3fMap ()).cast<double> () * row_off * off_surface_epsilon_);
81  }
82 
83  d (row_i, 0) = row_off * off_surface_epsilon_;
84  }
85 
86  // Solve for the weights
87  Eigen::MatrixXd w (2*N, 1);
88 
89  // Solve_linear_system (M, d, w);
90  w = M.fullPivLu ().solve (d);
91 
92  std::vector<double> weights (2*N);
93  std::vector<Eigen::Vector3d> centers (2*N);
94  for (unsigned int i = 0; i < N; ++i)
95  {
96  centers[i] = Eigen::Vector3f (input_->points[i].getVector3fMap ()).cast<double> ();
97  centers[i + N] = Eigen::Vector3f (input_->points[i].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[i].getNormalVector3fMap ()).cast<double> () * off_surface_epsilon_;
98  weights[i] = w (i, 0);
99  weights[i + N] = w (i + N, 0);
100  }
101 
102  for (int x = 0; x < res_x_; ++x)
103  for (int y = 0; y < res_y_; ++y)
104  for (int z = 0; z < res_z_; ++z)
105  {
106  Eigen::Vector3d point;
107  point[0] = min_p_[0] + (max_p_[0] - min_p_[0]) * float (x) / float (res_x_);
108  point[1] = min_p_[1] + (max_p_[1] - min_p_[1]) * float (y) / float (res_y_);
109  point[2] = min_p_[2] + (max_p_[2] - min_p_[2]) * float (z) / float (res_z_);
110 
111  double f = 0.0;
112  std::vector<double>::const_iterator w_it (weights.begin());
113  for (std::vector<Eigen::Vector3d>::const_iterator c_it = centers.begin ();
114  c_it != centers.end (); ++c_it, ++w_it)
115  f += *w_it * kernel (*c_it, point);
116 
117  grid_[x * res_y_*res_z_ + y * res_z_ + z] = float (f);
118  }
119 }
120 
121 //////////////////////////////////////////////////////////////////////////////////////////////
122 template <typename PointNT> double
123 pcl::MarchingCubesRBF<PointNT>::kernel (Eigen::Vector3d c, Eigen::Vector3d x)
124 {
125  double r = (x - c).norm ();
126  return (r * r * r);
127 }
128 
129 #define PCL_INSTANTIATE_MarchingCubesRBF(T) template class PCL_EXPORTS pcl::MarchingCubesRBF<T>;
130 
131 #endif // PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
132 
double kernel(Eigen::Vector3d c, Eigen::Vector3d x)
the Radial Basis Function kernel.
The marching cubes surface reconstruction algorithm.
MarchingCubesRBF()
Constructor.
void voxelizeData()
Convert the point cloud into voxel data.