Point Cloud Library (PCL)  1.9.1-dev
marching_cubes_hoppe.hpp
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35 
36 #ifndef PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
37 #define PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
38 
39 #include <pcl/surface/marching_cubes_hoppe.h>
40 #include <pcl/common/common.h>
41 #include <pcl/common/vector_average.h>
42 #include <pcl/Vertices.h>
43 
44 //////////////////////////////////////////////////////////////////////////////////////////////
45 template <typename PointNT>
47 {
48 }
49 
50 
51 //////////////////////////////////////////////////////////////////////////////////////////////
52 template <typename PointNT> void
54 {
55  const bool is_far_ignored = dist_ignore_ > 0.0f;
56 
57  for (int x = 0; x < res_x_; ++x)
58  {
59  const int y_start = x * res_y_ * res_z_;
60 
61  for (int y = 0; y < res_y_; ++y)
62  {
63  const int z_start = y_start + y * res_z_;
64 
65  for (int z = 0; z < res_z_; ++z)
66  {
67  std::vector<int> nn_indices (1, 0);
68  std::vector<float> nn_sqr_dists (1, 0.0f);
69  const Eigen::Vector3f point = (lower_boundary_ + size_voxel_ * Eigen::Array3f (x, y, z)).matrix ();
70  PointNT p;
71 
72  p.getVector3fMap () = point;
73 
74  tree_->nearestKSearch (p, 1, nn_indices, nn_sqr_dists);
75 
76  if (!is_far_ignored || nn_sqr_dists[0] < dist_ignore_)
77  {
78  const Eigen::Vector3f normal = input_->points[nn_indices[0]].getNormalVector3fMap ();
79 
80  if (!std::isnan (normal (0)) && normal.norm () > 0.5f)
81  grid_[z_start + z] = normal.dot (
82  point - input_->points[nn_indices[0]].getVector3fMap ());
83  }
84  }
85  }
86  }
87 }
88 
89 
90 
91 #define PCL_INSTANTIATE_MarchingCubesHoppe(T) template class PCL_EXPORTS pcl::MarchingCubesHoppe<T>;
92 
93 #endif // PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
94 
void voxelizeData() override
Convert the point cloud into voxel data.
Define standard C methods and C++ classes that are common to all methods.