normal_3d.hpp

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 * $Id: normal_3d.hpp 5026 2012-03-12 02:51:44Z rusu $
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#ifndef PCL_FEATURES_IMPL_NORMAL_3D_H_
#define PCL_FEATURES_IMPL_NORMAL_3D_H_

#include <pcl/features/normal_3d.h>

template <typename PointInT, typename PointOutT> void
pcl::NormalEstimation<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
  // Allocate enough space to hold the results
  // \note This resize is irrelevant for a radiusSearch ().
  std::vector<int> nn_indices (k_);
  std::vector<float> nn_dists (k_);

  output.is_dense = true;
  // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
  if (input_->is_dense)
  {
    // Iterating over the entire index vector
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        output.points[idx].normal[0] = output.points[idx].normal[1] = output.points[idx].normal[2] = output.points[idx].curvature = std::numeric_limits<float>::quiet_NaN ();

        output.is_dense = false;
        continue;
      }

      computePointNormal (*surface_, nn_indices,
                          output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2], output.points[idx].curvature);

      flipNormalTowardsViewpoint (input_->points[(*indices_)[idx]], vpx_, vpy_, vpz_,
                                  output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2]);

    }
  }
  else
  {
    // Iterating over the entire index vector
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (!isFinite ((*input_)[(*indices_)[idx]]) ||
          this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        output.points[idx].normal[0] = output.points[idx].normal[1] = output.points[idx].normal[2] = output.points[idx].curvature = std::numeric_limits<float>::quiet_NaN ();

        output.is_dense = false;
        continue;
      }

      computePointNormal (*surface_, nn_indices,
                          output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2], output.points[idx].curvature);

      flipNormalTowardsViewpoint (input_->points[(*indices_)[idx]], vpx_, vpy_, vpz_,
                                  output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2]);

    }
  }
}

template <typename PointInT> void
pcl::NormalEstimation<PointInT, Eigen::MatrixXf>::computeFeatureEigen (pcl::PointCloud<Eigen::MatrixXf> &output)
{
  // Resize the output dataset
  output.points.resize (indices_->size (), 4);

  // Allocate enough space to hold the results
  // \note This resize is irrelevant for a radiusSearch ().
  std::vector<int> nn_indices (k_);
  std::vector<float> nn_dists (k_);

  output.is_dense = true;
  // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
  if (input_->is_dense)
  {
    // Iterating over the entire index vector
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        output.points (idx, 0) = output.points (idx, 1) = output.points (idx, 2) = output.points (idx, 3) = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
        continue;
      }

      computePointNormal (*surface_, nn_indices,
                          output.points (idx, 0), output.points (idx, 1), output.points (idx, 2), output.points (idx, 3));

      flipNormalTowardsViewpoint (input_->points[(*indices_)[idx]], vpx_, vpy_, vpz_,
                                  output.points (idx, 0), output.points (idx, 1), output.points (idx, 2));

    }
  }
  else
  {
    // Iterating over the entire index vector
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (!isFinite ((*input_)[(*indices_)[idx]]) ||
          this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        output.points (idx, 0) = output.points (idx, 1) = output.points (idx, 2) = output.points (idx, 3) = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
        continue;
      }

      computePointNormal (*surface_, nn_indices,
                          output.points (idx, 0), output.points (idx, 1), output.points (idx, 2), output.points (idx, 3));

      flipNormalTowardsViewpoint (input_->points[(*indices_)[idx]], vpx_, vpy_, vpz_,
                                  output.points (idx, 0), output.points (idx, 1), output.points (idx, 2));

    }
  }
}

#define PCL_INSTANTIATE_NormalEstimation(T,NT) template class PCL_EXPORTS pcl::NormalEstimation<T,NT>;

#endif    // PCL_FEATURES_IMPL_NORMAL_3D_H_