Point Cloud Library (PCL) 1.14.0
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principal_curvatures.h
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40
41#pragma once
42
43#include <pcl/features/feature.h>
44
45namespace 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> >;
64 using Feature<PointInT, PointOutT>::feature_name_;
65 using Feature<PointInT, PointOutT>::getClassName;
66 using Feature<PointInT, PointOutT>::indices_;
67 using Feature<PointInT, PointOutT>::k_;
68 using Feature<PointInT, PointOutT>::search_parameter_;
69 using Feature<PointInT, PointOutT>::surface_;
70 using Feature<PointInT, PointOutT>::input_;
71 using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
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
PointCloudNConstPtr normals_
A pointer to the input dataset that contains the point normals of the XYZ dataset.
Definition feature.h:349
Feature represents the base feature class.
Definition feature.h:107
double search_parameter_
The actual search parameter (from either search_radius_ or k_).
Definition feature.h:234
const std::string & getClassName() const
Get a string representation of the name of this class.
Definition feature.h:244
int k_
The number of K nearest neighbors to use for each point.
Definition feature.h:240
std::string feature_name_
The feature name.
Definition feature.h:220
PointCloudInConstPtr surface_
An input point cloud describing the surface that is to be used for nearest neighbors estimation.
Definition feature.h:228
PointCloudConstPtr input_
The input point cloud dataset.
Definition pcl_base.h:147
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition pcl_base.h:150
PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of...
shared_ptr< PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT > > Ptr
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.
shared_ptr< const PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT > > ConstPtr
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