template <typename PointNT> void
pcl::MarchingCubesHoppe<PointNT>::voxelizeData ()
{
for (int x = 0; x < res_x_; ++x)
for (int y = 0; y < res_y_; ++y)
for (int z = 0; z < res_z_; ++z)
{
std::vector<int> nn_indices;
std::vector<float> nn_sqr_dists;
Eigen::Vector3f point;
point[0] = min_p_[0] + (max_p_[0] - min_p_[0]) * float (x) / float (res_x_);
point[1] = min_p_[1] + (max_p_[1] - min_p_[1]) * float (y) / float (res_y_);
point[2] = min_p_[2] + (max_p_[2] - min_p_[2]) * float (z) / float (res_z_);
PointNT p;
p.getVector3fMap () = point;
tree_->nearestKSearch (p, 1, nn_indices, nn_sqr_dists);
grid_[x * res_y_*res_z_ + y * res_z_ + z] = input_->points[nn_indices[0]].getNormalVector3fMap ().dot (
point - input_->points[nn_indices[0]].getVector3fMap ());
}
}
template <typename PointNT> void
pcl::MarchingCubesHoppe<PointNT>::voxelizeData ()
{
const bool is_far_ignored = dist_ignore_ > 0.0f;
for (int x = 0; x < res_x_; ++x)
{
const int y_start = x * res_y_ * res_z_;
for (int y = 0; y < res_y_; ++y)
{
const int z_start = y_start + y * res_z_;
for (int z = 0; z < res_z_; ++z)
{
std::vector<int> nn_indices (1, 0);
std::vector<float> nn_sqr_dists (1, 0.0f);
const Eigen::Vector3f point = (lower_boundary_ + size_voxel_ * Eigen::Array3f (x, y, z)).matrix ();
PointNT p;
p.getVector3fMap () = point;
tree_->nearestKSearch (p, 1, nn_indices, nn_sqr_dists);
if (!is_far_ignored || nn_sqr_dists[0] < dist_ignore_)
{
const Eigen::Vector3f normal = input_->points[nn_indices[0]].getNormalVector3fMap ();
if (!std::isnan (normal (0)) && normal.norm () > 0.5f)
grid_[z_start + z] = normal.dot (
point - input_->points[nn_indices[0]].getVector3fMap ());
}
}
}
}
}