Point Cloud Library (PCL)  1.3.1
principal_curvatures.hpp
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00034  * $Id: principal_curvatures.hpp 3176 2011-11-17 14:32:07Z rusu $
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00037 
00038 #ifndef PCL_FEATURES_IMPL_PRINCIPAL_CURVATURES_H_
00039 #define PCL_FEATURES_IMPL_PRINCIPAL_CURVATURES_H_
00040 
00041 #include "pcl/features/principal_curvatures.h"
00042 
00044 template <typename PointInT, typename PointNT, typename PointOutT> void
00045 pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computePointPrincipalCurvatures (
00046       const pcl::PointCloud<PointNT> &normals, int p_idx, const std::vector<int> &indices,
00047       float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
00048 {
00049   EIGEN_ALIGN16 Eigen::Matrix3f I = Eigen::Matrix3f::Identity ();
00050   Eigen::Vector3f n_idx (normals.points[p_idx].normal[0], normals.points[p_idx].normal[1], normals.points[p_idx].normal[2]);
00051   EIGEN_ALIGN16 Eigen::Matrix3f M = I - n_idx * n_idx.transpose ();    // projection matrix (into tangent plane)
00052 
00053   // Project normals into the tangent plane
00054   Eigen::Vector3f normal;
00055   projected_normals_.resize (indices.size ());
00056   xyz_centroid_.setZero ();
00057   for (size_t idx = 0; idx < indices.size(); ++idx)
00058   {
00059     normal[0] = normals.points[indices[idx]].normal[0];
00060     normal[1] = normals.points[indices[idx]].normal[1];
00061     normal[2] = normals.points[indices[idx]].normal[2];
00062 
00063     projected_normals_[idx] = M * normal;
00064     xyz_centroid_ += projected_normals_[idx];
00065   }
00066 
00067   // Estimate the XYZ centroid
00068   xyz_centroid_ /= indices.size ();
00069 
00070   // Initialize to 0
00071   covariance_matrix_.setZero ();
00072 
00073   double demean_xy, demean_xz, demean_yz;
00074   // For each point in the cloud
00075   for (size_t idx = 0; idx < indices.size (); ++idx)
00076   {
00077     demean_ = projected_normals_[idx] - xyz_centroid_;
00078 
00079     demean_xy = demean_[0] * demean_[1];
00080     demean_xz = demean_[0] * demean_[2];
00081     demean_yz = demean_[1] * demean_[2];
00082 
00083     covariance_matrix_(0, 0) += demean_[0] * demean_[0];
00084     covariance_matrix_(0, 1) += demean_xy;
00085     covariance_matrix_(0, 2) += demean_xz;
00086 
00087     covariance_matrix_(1, 0) += demean_xy;
00088     covariance_matrix_(1, 1) += demean_[1] * demean_[1];
00089     covariance_matrix_(1, 2) += demean_yz;
00090 
00091     covariance_matrix_(2, 0) += demean_xz;
00092     covariance_matrix_(2, 1) += demean_yz;
00093     covariance_matrix_(2, 2) += demean_[2] * demean_[2];
00094   }
00095 
00096   // Extract the eigenvalues and eigenvectors
00097   //Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> ei_symm (covariance_matrix_);
00098   //eigenvalues_  = ei_symm.eigenvalues ();
00099   //eigenvectors_ = ei_symm.eigenvectors ();
00100   pcl::eigen33 (covariance_matrix_, eigenvectors_, eigenvalues_);
00101  
00102   pcx = eigenvectors_ (0, 2);
00103   pcy = eigenvectors_ (1, 2);
00104   pcz = eigenvectors_ (2, 2);
00105   float indices_size = 1.0f / indices.size ();
00106   pc1 = eigenvalues_ (2) * indices_size;
00107   pc2 = eigenvalues_ (1) * indices_size;
00108 }
00109 
00110 
00112 template <typename PointInT, typename PointNT, typename PointOutT> void
00113 pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
00114 {
00115   // Allocate enough space to hold the results
00116   // \note This resize is irrelevant for a radiusSearch ().
00117   std::vector<int> nn_indices (k_);
00118   std::vector<float> nn_dists (k_);
00119 
00120   // Iterating over the entire index vector
00121   for (size_t idx = 0; idx < indices_->size (); ++idx)
00122   {
00123     this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists);
00124 
00125     // Estimate the principal curvatures at each patch
00126     computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices,
00127                                      output.points[idx].principal_curvature[0], output.points[idx].principal_curvature[1], output.points[idx].principal_curvature[2],
00128                                      output.points[idx].pc1, output.points[idx].pc2);
00129   }
00130 }
00131 
00132 #define PCL_INSTANTIATE_PrincipalCurvaturesEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PrincipalCurvaturesEstimation<T,NT,OutT>;
00133 
00134 #endif    // PCL_FEATURES_IMPL_PRINCIPAL_CURVATURES_H_ 
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