normal_3d_omp.hpp

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 * $Id: normal_3d_omp.hpp 5026 2012-03-12 02:51:44Z rusu $
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#ifndef PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_
#define PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_

#include <pcl/features/normal_3d_omp.h>

template <typename PointInT> void
pcl::NormalEstimationOMP<PointInT, Eigen::MatrixXf>::computeFeatureEigen (pcl::PointCloud<Eigen::MatrixXf> &output)
{
  float vpx, vpy, vpz;
  getViewPoint (vpx, vpy, vpz);
  output.is_dense = true;

  // Resize the output dataset
  output.points.resize (indices_->size (), 4);

  // GCC 4.2.x seems to segfault with "internal compiler error" on MacOS X here
#if defined(_WIN32) || ((__GNUC__ > 4) && (__GNUC_MINOR__ > 2)) 
#pragma omp parallel for schedule (dynamic, threads_)
#endif
  // Iterating over the entire index vector
  for (int idx = 0; idx < static_cast<int> (indices_->size ()); ++idx)
  {
    // 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_);

    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;
    }

    // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
    Eigen::Vector4f xyz_centroid;
    // Estimate the XYZ centroid
    compute3DCentroid (*surface_, nn_indices, xyz_centroid);

    // Placeholder for the 3x3 covariance matrix at each surface patch
    EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
    // Compute the 3x3 covariance matrix
    computeCovarianceMatrix (*surface_, nn_indices, xyz_centroid, covariance_matrix);

    // Get the plane normal and surface curvature
    solvePlaneParameters (covariance_matrix,
                          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));
  }
}

template <typename PointInT, typename PointOutT> void
pcl::NormalEstimationOMP<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
  float vpx, vpy, vpz;
  getViewPoint (vpx, vpy, vpz);

  output.is_dense = true;
  // Iterating over the entire index vector
#pragma omp parallel for schedule (dynamic, threads_)
  for (int idx = 0; idx < static_cast<int> (indices_->size ()); ++idx)
  {
    // 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_);

    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;
    }

    // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
    Eigen::Vector4f xyz_centroid;
    // Estimate the XYZ centroid
    compute3DCentroid (*surface_, nn_indices, xyz_centroid);

    // Placeholder for the 3x3 covariance matrix at each surface patch
    EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
    // Compute the 3x3 covariance matrix
    computeCovarianceMatrix (*surface_, nn_indices, xyz_centroid, covariance_matrix);

    // Get the plane normal and surface curvature
    solvePlaneParameters (covariance_matrix,
                          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]);
  }
}

#define PCL_INSTANTIATE_NormalEstimationOMP(T,NT) template class PCL_EXPORTS pcl::NormalEstimationOMP<T,NT>;

#endif    // PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_