Point Cloud Library (PCL)  1.9.1
principal_curvatures.h
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40 
41 #ifndef PCL_PRINCIPAL_CURVATURES_H_
42 #define PCL_PRINCIPAL_CURVATURES_H_
43 
44 #include <pcl/features/eigen.h>
45 #include <pcl/features/feature.h>
46 
47 namespace pcl
48 {
49  /** \brief PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of
50  * principal surface curvatures for a given point cloud dataset containing points and normals.
51  *
52  * The recommended PointOutT is pcl::PrincipalCurvatures.
53  *
54  * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
55  * \ref NormalEstimationOMP for an example on how to extend this to parallel implementations.
56  *
57  * \author Radu B. Rusu, Jared Glover
58  * \ingroup features
59  */
60  template <typename PointInT, typename PointNT, typename PointOutT = pcl::PrincipalCurvatures>
61  class PrincipalCurvaturesEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
62  {
63  public:
64  typedef boost::shared_ptr<PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> > Ptr;
65  typedef boost::shared_ptr<const PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> > ConstPtr;
74 
77 
78  /** \brief Empty constructor. */
80  projected_normals_ (),
81  xyz_centroid_ (Eigen::Vector3f::Zero ()),
82  demean_ (Eigen::Vector3f::Zero ()),
83  covariance_matrix_ (Eigen::Matrix3f::Zero ()),
84  eigenvector_ (Eigen::Vector3f::Zero ()),
85  eigenvalues_ (Eigen::Vector3f::Zero ())
86  {
87  feature_name_ = "PrincipalCurvaturesEstimation";
88  };
89 
90  /** \brief Perform Principal Components Analysis (PCA) on the point normals of a surface patch in the tangent
91  * plane of the given point normal, and return the principal curvature (eigenvector of the max eigenvalue),
92  * along with both the max (pc1) and min (pc2) eigenvalues
93  * \param[in] normals the point cloud normals
94  * \param[in] p_idx the query point at which the least-squares plane was estimated
95  * \param[in] indices the point cloud indices that need to be used
96  * \param[out] pcx the principal curvature X direction
97  * \param[out] pcy the principal curvature Y direction
98  * \param[out] pcz the principal curvature Z direction
99  * \param[out] pc1 the max eigenvalue of curvature
100  * \param[out] pc2 the min eigenvalue of curvature
101  */
102  void
104  int p_idx, const std::vector<int> &indices,
105  float &pcx, float &pcy, float &pcz, float &pc1, float &pc2);
106 
107  protected:
108 
109  /** \brief Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1)
110  * and min (pc2) eigenvalues for all points given in <setInputCloud (), setIndices ()> using the surface in
111  * setSearchSurface () and the spatial locator in setSearchMethod ()
112  * \param[out] output the resultant point cloud model dataset that contains the principal curvature estimates
113  */
114  void
115  computeFeature (PointCloudOut &output);
116 
117  private:
118  /** \brief A pointer to the input dataset that contains the point normals of the XYZ dataset. */
119  std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > projected_normals_;
120 
121  /** \brief SSE aligned placeholder for the XYZ centroid of a surface patch. */
122  Eigen::Vector3f xyz_centroid_;
123 
124  /** \brief Temporary point placeholder. */
125  Eigen::Vector3f demean_;
126 
127  /** \brief Placeholder for the 3x3 covariance matrix at each surface patch. */
128  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_;
129 
130  /** \brief SSE aligned eigenvectors placeholder for a covariance matrix. */
131  Eigen::Vector3f eigenvector_;
132  /** \brief eigenvalues placeholder for a covariance matrix. */
133  Eigen::Vector3f eigenvalues_;
134  };
135 }
136 
137 #ifdef PCL_NO_PRECOMPILE
138 #include <pcl/features/impl/principal_curvatures.hpp>
139 #endif
140 
141 #endif //#ifndef PCL_PRINCIPAL_CURVATURES_H_
PrincipalCurvaturesEstimation()
Empty constructor.
struct pcl::PointXYZIEdge EIGEN_ALIGN16
pcl::PointCloud< PointInT > PointCloudIn
std::string feature_name_
The feature name.
Definition: feature.h:222
This file defines compatibility wrappers for low level I/O functions.
Definition: convolution.h:45
void computePointPrincipalCurvatures(const pcl::PointCloud< PointNT > &normals, int p_idx, const std::vector< int > &indices, float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
Perform Principal Components Analysis (PCA) on the point normals of a surface patch in the tangent pl...
boost::shared_ptr< PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT > > Ptr
Definition: bfgs.h:10
Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
PointCloud represents the base class in PCL for storing collections of 3D points. ...
void computeFeature(PointCloudOut &output)
Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1) a...
Feature represents the base feature class.
Definition: feature.h:105
boost::shared_ptr< const PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT > > ConstPtr
PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of...