Point Cloud Library (PCL)  1.7.0
/tmp/buildd/pcl-1.7-1.7.0/features/include/pcl/features/principal_curvatures.h
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00040 
00041 #ifndef PCL_PRINCIPAL_CURVATURES_H_
00042 #define PCL_PRINCIPAL_CURVATURES_H_
00043 
00044 #include <pcl/features/eigen.h>
00045 #include <pcl/features/feature.h>
00046 
00047 namespace pcl
00048 {
00049   /** \brief PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of
00050     * principal surface curvatures for a given point cloud dataset containing points and normals.
00051     *
00052     * The recommended PointOutT is pcl::PrincipalCurvatures.
00053     *
00054     * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
00055     * \ref NormalEstimationOMP for an example on how to extend this to parallel implementations.
00056     *
00057     * \author Radu B. Rusu, Jared Glover
00058     * \ingroup features
00059     */
00060   template <typename PointInT, typename PointNT, typename PointOutT = pcl::PrincipalCurvatures>
00061   class PrincipalCurvaturesEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
00062   {
00063     public:
00064       typedef boost::shared_ptr<PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> > Ptr;
00065       typedef boost::shared_ptr<const PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> > ConstPtr;
00066       using Feature<PointInT, PointOutT>::feature_name_;
00067       using Feature<PointInT, PointOutT>::getClassName;
00068       using Feature<PointInT, PointOutT>::indices_;
00069       using Feature<PointInT, PointOutT>::k_;
00070       using Feature<PointInT, PointOutT>::search_parameter_;
00071       using Feature<PointInT, PointOutT>::surface_;
00072       using Feature<PointInT, PointOutT>::input_;
00073       using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
00074 
00075       typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;
00076       typedef pcl::PointCloud<PointInT> PointCloudIn;
00077 
00078       /** \brief Empty constructor. */
00079       PrincipalCurvaturesEstimation () : 
00080         projected_normals_ (), 
00081         xyz_centroid_ (Eigen::Vector3f::Zero ()), 
00082         demean_ (Eigen::Vector3f::Zero ()),
00083         covariance_matrix_ (Eigen::Matrix3f::Zero ()),
00084         eigenvector_ (Eigen::Vector3f::Zero ()),
00085         eigenvalues_ (Eigen::Vector3f::Zero ())
00086       {
00087         feature_name_ = "PrincipalCurvaturesEstimation";
00088       };
00089 
00090       /** \brief Perform Principal Components Analysis (PCA) on the point normals of a surface patch in the tangent
00091        *  plane of the given point normal, and return the principal curvature (eigenvector of the max eigenvalue),
00092        *  along with both the max (pc1) and min (pc2) eigenvalues
00093        * \param[in] normals the point cloud normals
00094        * \param[in] p_idx the query point at which the least-squares plane was estimated
00095        * \param[in] indices the point cloud indices that need to be used
00096        * \param[out] pcx the principal curvature X direction
00097        * \param[out] pcy the principal curvature Y direction
00098        * \param[out] pcz the principal curvature Z direction
00099        * \param[out] pc1 the max eigenvalue of curvature
00100        * \param[out] pc2 the min eigenvalue of curvature
00101        */
00102       void
00103       computePointPrincipalCurvatures (const pcl::PointCloud<PointNT> &normals,
00104                                        int p_idx, const std::vector<int> &indices,
00105                                        float &pcx, float &pcy, float &pcz, float &pc1, float &pc2);
00106 
00107     protected:
00108 
00109       /** \brief Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1)
00110         * and min (pc2) eigenvalues for all points given in <setInputCloud (), setIndices ()> using the surface in
00111         * setSearchSurface () and the spatial locator in setSearchMethod ()
00112         * \param[out] output the resultant point cloud model dataset that contains the principal curvature estimates
00113         */
00114       void
00115       computeFeature (PointCloudOut &output);
00116 
00117     private:
00118       /** \brief A pointer to the input dataset that contains the point normals of the XYZ dataset. */
00119       std::vector<Eigen::Vector3f> projected_normals_;
00120 
00121       /** \brief SSE aligned placeholder for the XYZ centroid of a surface patch. */
00122       Eigen::Vector3f xyz_centroid_;
00123 
00124       /** \brief Temporary point placeholder. */
00125       Eigen::Vector3f demean_;
00126 
00127       /** \brief Placeholder for the 3x3 covariance matrix at each surface patch. */
00128       EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_;
00129 
00130       /** \brief SSE aligned eigenvectors placeholder for a covariance matrix. */
00131       Eigen::Vector3f eigenvector_;
00132       /** \brief eigenvalues placeholder for a covariance matrix. */
00133       Eigen::Vector3f eigenvalues_;
00134   };
00135 }
00136 
00137 #ifdef PCL_NO_PRECOMPILE
00138 #include <pcl/features/impl/principal_curvatures.hpp>
00139 #endif
00140 
00141 #endif  //#ifndef PCL_PRINCIPAL_CURVATURES_H_