Point Cloud Library (PCL)  1.12.1-dev
principal_curvatures.h
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40 
41 #pragma once
42 
43 #include <pcl/features/feature.h>
44 
45 namespace pcl
46 {
47  /** \brief PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of
48  * principal surface curvatures for a given point cloud dataset containing points and normals.
49  *
50  * The recommended PointOutT is pcl::PrincipalCurvatures.
51  *
52  * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
53  * \ref NormalEstimationOMP for an example on how to extend this to parallel implementations.
54  *
55  * \author Radu B. Rusu, Jared Glover
56  * \ingroup features
57  */
58  template <typename PointInT, typename PointNT, typename PointOutT = pcl::PrincipalCurvatures>
59  class PrincipalCurvaturesEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
60  {
61  public:
62  using Ptr = shared_ptr<PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> >;
63  using ConstPtr = shared_ptr<const PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> >;
72 
75 
76  /** \brief Empty constructor. */
78  xyz_centroid_ (Eigen::Vector3f::Zero ()),
79  demean_ (Eigen::Vector3f::Zero ()),
80  covariance_matrix_ (Eigen::Matrix3f::Zero ()),
81  eigenvector_ (Eigen::Vector3f::Zero ()),
82  eigenvalues_ (Eigen::Vector3f::Zero ())
83  {
84  feature_name_ = "PrincipalCurvaturesEstimation";
85  };
86 
87  /** \brief Perform Principal Components Analysis (PCA) on the point normals of a surface patch in the tangent
88  * plane of the given point normal, and return the principal curvature (eigenvector of the max eigenvalue),
89  * along with both the max (pc1) and min (pc2) eigenvalues
90  * \param[in] normals the point cloud normals
91  * \param[in] p_idx the query point at which the least-squares plane was estimated
92  * \param[in] indices the point cloud indices that need to be used
93  * \param[out] pcx the principal curvature X direction
94  * \param[out] pcy the principal curvature Y direction
95  * \param[out] pcz the principal curvature Z direction
96  * \param[out] pc1 the max eigenvalue of curvature
97  * \param[out] pc2 the min eigenvalue of curvature
98  */
99  void
101  int p_idx, const pcl::Indices &indices,
102  float &pcx, float &pcy, float &pcz, float &pc1, float &pc2);
103 
104  protected:
105 
106  /** \brief Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1)
107  * and min (pc2) eigenvalues for all points given in <setInputCloud (), setIndices ()> using the surface in
108  * setSearchSurface () and the spatial locator in setSearchMethod ()
109  * \param[out] output the resultant point cloud model dataset that contains the principal curvature estimates
110  */
111  void
112  computeFeature (PointCloudOut &output) override;
113 
114  private:
115  /** \brief A pointer to the input dataset that contains the point normals of the XYZ dataset. */
116  std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > projected_normals_;
117 
118  /** \brief SSE aligned placeholder for the XYZ centroid of a surface patch. */
119  Eigen::Vector3f xyz_centroid_;
120 
121  /** \brief Temporary point placeholder. */
122  Eigen::Vector3f demean_;
123 
124  /** \brief Placeholder for the 3x3 covariance matrix at each surface patch. */
125  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_;
126 
127  /** \brief SSE aligned eigenvectors placeholder for a covariance matrix. */
128  Eigen::Vector3f eigenvector_;
129  /** \brief eigenvalues placeholder for a covariance matrix. */
130  Eigen::Vector3f eigenvalues_;
131  };
132 }
133 
134 #ifdef PCL_NO_PRECOMPILE
135 #include <pcl/features/impl/principal_curvatures.hpp>
136 #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:220
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 pcl::Indices &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
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133