principal_curvatures.hpp

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 * $Id: principal_curvatures.hpp 3755 2011-12-31 23:45:30Z rusu $
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#ifndef PCL_FEATURES_IMPL_PRINCIPAL_CURVATURES_H_
#define PCL_FEATURES_IMPL_PRINCIPAL_CURVATURES_H_

#include "pcl/features/principal_curvatures.h"

template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computePointPrincipalCurvatures (
      const pcl::PointCloud<PointNT> &normals, int p_idx, const std::vector<int> &indices,
      float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
{
  EIGEN_ALIGN16 Eigen::Matrix3f I = Eigen::Matrix3f::Identity ();
  Eigen::Vector3f n_idx (normals.points[p_idx].normal[0], normals.points[p_idx].normal[1], normals.points[p_idx].normal[2]);
  EIGEN_ALIGN16 Eigen::Matrix3f M = I - n_idx * n_idx.transpose ();    // projection matrix (into tangent plane)

  // Project normals into the tangent plane
  Eigen::Vector3f normal;
  projected_normals_.resize (indices.size ());
  xyz_centroid_.setZero ();
  for (size_t idx = 0; idx < indices.size(); ++idx)
  {
    normal[0] = normals.points[indices[idx]].normal[0];
    normal[1] = normals.points[indices[idx]].normal[1];
    normal[2] = normals.points[indices[idx]].normal[2];

    projected_normals_[idx] = M * normal;
    xyz_centroid_ += projected_normals_[idx];
  }

  // Estimate the XYZ centroid
  xyz_centroid_ /= indices.size ();

  // Initialize to 0
  covariance_matrix_.setZero ();

  double demean_xy, demean_xz, demean_yz;
  // For each point in the cloud
  for (size_t idx = 0; idx < indices.size (); ++idx)
  {
    demean_ = projected_normals_[idx] - xyz_centroid_;

    demean_xy = demean_[0] * demean_[1];
    demean_xz = demean_[0] * demean_[2];
    demean_yz = demean_[1] * demean_[2];

    covariance_matrix_(0, 0) += demean_[0] * demean_[0];
    covariance_matrix_(0, 1) += demean_xy;
    covariance_matrix_(0, 2) += demean_xz;

    covariance_matrix_(1, 0) += demean_xy;
    covariance_matrix_(1, 1) += demean_[1] * demean_[1];
    covariance_matrix_(1, 2) += demean_yz;

    covariance_matrix_(2, 0) += demean_xz;
    covariance_matrix_(2, 1) += demean_yz;
    covariance_matrix_(2, 2) += demean_[2] * demean_[2];
  }

  // Extract the eigenvalues and eigenvectors
  //Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> ei_symm (covariance_matrix_);
  //eigenvalues_  = ei_symm.eigenvalues ();
  //eigenvectors_ = ei_symm.eigenvectors ();
  pcl::eigen33 (covariance_matrix_, eigenvectors_, eigenvalues_);
 
  pcx = eigenvectors_ (0, 2);
  pcy = eigenvectors_ (1, 2);
  pcz = eigenvectors_ (2, 2);
  float indices_size = 1.0f / indices.size ();
  pc1 = eigenvalues_ (2) * indices_size;
  pc2 = eigenvalues_ (1) * indices_size;
}


template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, 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].principal_curvature[0] = output.points[idx].principal_curvature[1] = output.points[idx].principal_curvature[2] = 
          output.points[idx].pc1 = output.points[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
        continue;
      }

      // Estimate the principal curvatures at each patch
      computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
                                       output.points[idx].principal_curvature[0], output.points[idx].principal_curvature[1], output.points[idx].principal_curvature[2],
                                       output.points[idx].pc1, output.points[idx].pc2);
    }
  }
  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].principal_curvature[0] = output.points[idx].principal_curvature[1] = output.points[idx].principal_curvature[2] = 
          output.points[idx].pc1 = output.points[idx].pc2 = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
        continue;
      }

      // Estimate the principal curvatures at each patch
      computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
                                       output.points[idx].principal_curvature[0], output.points[idx].principal_curvature[1], output.points[idx].principal_curvature[2],
                                       output.points[idx].pc1, output.points[idx].pc2);
    }
  }
}
template <typename PointInT, typename PointNT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, Eigen::MatrixXf>::computeFeature (pcl::PointCloud<Eigen::MatrixXf> &output)
{
  // Resize the output dataset
  output.points.resize (indices_->size (), 5);

  // 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.row (idx).setConstant (std::numeric_limits<float>::quiet_NaN ());
        output.is_dense = false;
        continue;
      }

      // Estimate the principal curvatures at each patch
      this->computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
                                       output.points (idx, 0), output.points (idx, 1), output.points (idx, 2),
                                       output.points (idx, 3), output.points (idx, 4));
    }
  }
  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.row (idx).setConstant (std::numeric_limits<float>::quiet_NaN ());
        output.is_dense = false;
        continue;
      }

      // Estimate the principal curvatures at each patch
      this->computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
                                       output.points (idx, 0), output.points (idx, 1), output.points (idx, 2),
                                       output.points (idx, 3), output.points (idx, 4));
    }
  }
}

#define PCL_INSTANTIATE_PrincipalCurvaturesEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PrincipalCurvaturesEstimation<T,NT,OutT>;

#endif    // PCL_FEATURES_IMPL_PRINCIPAL_CURVATURES_H_