Point Cloud Library (PCL)  1.11.0
principal_curvatures.h
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40 
41 #pragma once
42 
43 #include <pcl/features/eigen.h>
44 #include <pcl/features/feature.h>
45 
46 namespace pcl
47 {
48  /** \brief PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of
49  * principal surface curvatures for a given point cloud dataset containing points and normals.
50  *
51  * The recommended PointOutT is pcl::PrincipalCurvatures.
52  *
53  * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
54  * \ref NormalEstimationOMP for an example on how to extend this to parallel implementations.
55  *
56  * \author Radu B. Rusu, Jared Glover
57  * \ingroup features
58  */
59  template <typename PointInT, typename PointNT, typename PointOutT = pcl::PrincipalCurvatures>
60  class PrincipalCurvaturesEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
61  {
62  public:
63  using Ptr = shared_ptr<PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> >;
64  using ConstPtr = shared_ptr<const PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> >;
73 
76 
77  /** \brief Empty constructor. */
79  xyz_centroid_ (Eigen::Vector3f::Zero ()),
80  demean_ (Eigen::Vector3f::Zero ()),
81  covariance_matrix_ (Eigen::Matrix3f::Zero ()),
82  eigenvector_ (Eigen::Vector3f::Zero ()),
83  eigenvalues_ (Eigen::Vector3f::Zero ())
84  {
85  feature_name_ = "PrincipalCurvaturesEstimation";
86  };
87 
88  /** \brief Perform Principal Components Analysis (PCA) on the point normals of a surface patch in the tangent
89  * plane of the given point normal, and return the principal curvature (eigenvector of the max eigenvalue),
90  * along with both the max (pc1) and min (pc2) eigenvalues
91  * \param[in] normals the point cloud normals
92  * \param[in] p_idx the query point at which the least-squares plane was estimated
93  * \param[in] indices the point cloud indices that need to be used
94  * \param[out] pcx the principal curvature X direction
95  * \param[out] pcy the principal curvature Y direction
96  * \param[out] pcz the principal curvature Z direction
97  * \param[out] pc1 the max eigenvalue of curvature
98  * \param[out] pc2 the min eigenvalue of curvature
99  */
100  void
102  int p_idx, const std::vector<int> &indices,
103  float &pcx, float &pcy, float &pcz, float &pc1, float &pc2);
104 
105  protected:
106 
107  /** \brief Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1)
108  * and min (pc2) eigenvalues for all points given in <setInputCloud (), setIndices ()> using the surface in
109  * setSearchSurface () and the spatial locator in setSearchMethod ()
110  * \param[out] output the resultant point cloud model dataset that contains the principal curvature estimates
111  */
112  void
113  computeFeature (PointCloudOut &output) override;
114 
115  private:
116  /** \brief A pointer to the input dataset that contains the point normals of the XYZ dataset. */
117  std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > projected_normals_;
118 
119  /** \brief SSE aligned placeholder for the XYZ centroid of a surface patch. */
120  Eigen::Vector3f xyz_centroid_;
121 
122  /** \brief Temporary point placeholder. */
123  Eigen::Vector3f demean_;
124 
125  /** \brief Placeholder for the 3x3 covariance matrix at each surface patch. */
126  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_;
127 
128  /** \brief SSE aligned eigenvectors placeholder for a covariance matrix. */
129  Eigen::Vector3f eigenvector_;
130  /** \brief eigenvalues placeholder for a covariance matrix. */
131  Eigen::Vector3f eigenvalues_;
132  };
133 }
134 
135 #ifdef PCL_NO_PRECOMPILE
136 #include <pcl/features/impl/principal_curvatures.hpp>
137 #endif
Feature represents the base feature class.
Definition: feature.h:107
shared_ptr< Feature< PointInT, PointOutT > > Ptr
Definition: feature.h:114
std::string feature_name_
The feature name.
Definition: feature.h:223
shared_ptr< const Feature< PointInT, PointOutT > > ConstPtr
Definition: feature.h:115
PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of...
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...
PrincipalCurvaturesEstimation()
Empty constructor.
void computeFeature(PointCloudOut &output) override
Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1) a...
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: bfgs.h:10