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#ifndef PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
#define PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_

#include <pcl/surface/marching_cubes_hoppe.h>
#include <pcl/common/common.h>
#include <pcl/common/vector_average.h>
#include <pcl/Vertices.h>
#include <pcl/kdtree/kdtree_flann.h>

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointNT>
pcl::MarchingCubesHoppe<PointNT>::~MarchingCubesHoppe ()
{
}


//////////////////////////////////////////////////////////////////////////////////////////////
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 ());
        }
      }
    }
  }
}



#define PCL_INSTANTIATE_MarchingCubesHoppe(T) template class PCL_EXPORTS pcl::MarchingCubesHoppe<T>;

#endif    // PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_

