Point Cloud Library (PCL) 1.12.0
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rsd.h
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40
41#pragma once
42
43#include <pcl/memory.h>
44#include <pcl/pcl_macros.h>
45#include <pcl/features/feature.h>
46
47namespace pcl
48{
49 /** \brief Transform a list of 2D matrices into a point cloud containing the values in a vector (Histogram<N>).
50 * Can be used to transform the 2D histograms obtained in \ref RSDEstimation into a point cloud.
51 * @note The template parameter N should be (greater or) equal to the product of the number of rows and columns.
52 * \param[in] histograms2D the list of neighborhood 2D histograms
53 * \param[out] histogramsPC the dataset containing the linearized matrices
54 * \ingroup features
55 */
56 template <int N> void
57 getFeaturePointCloud (const std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > &histograms2D, PointCloud<Histogram<N> > &histogramsPC)
58 {
59 histogramsPC.resize (histograms2D.size ());
60 histogramsPC.width = histograms2D.size ();
61 histogramsPC.height = 1;
62 histogramsPC.is_dense = true;
63
64 const int rows = histograms2D.at(0).rows();
65 const int cols = histograms2D.at(0).cols();
66
67 typename PointCloud<Histogram<N> >::VectorType::iterator it = histogramsPC.begin ();
68 for (const Eigen::MatrixXf& h : histograms2D)
69 {
70 Eigen::Map<Eigen::MatrixXf> histogram (&(it->histogram[0]), rows, cols);
71 histogram = h;
72 ++it;
73 }
74 }
75
76 /** \brief Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals
77 * \param[in] surface the dataset containing the XYZ points
78 * \param[in] normals the dataset containing the surface normals at each point in the dataset
79 * \param[in] indices the neighborhood point indices in the dataset (first point is used as the reference)
80 * \param[in] max_dist the upper bound for the considered distance interval
81 * \param[in] nr_subdiv the number of subdivisions for the considered distance interval
82 * \param[in] plane_radius maximum radius, above which everything can be considered planar
83 * \param[in] radii the output point of a type that should have r_min and r_max fields
84 * \param[in] compute_histogram if not false, the full neighborhood histogram is provided, usable as a point signature
85 * \ingroup features
86 */
87 template <typename PointInT, typename PointNT, typename PointOutT> Eigen::MatrixXf
89 const pcl::Indices &indices, double max_dist,
90 int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram = false);
91
92 /** \brief Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals
93 * \param[in] normals the dataset containing the surface normals at each point in the dataset
94 * \param[in] indices the neighborhood point indices in the dataset (first point is used as the reference)
95 * \param[in] sqr_dists the squared distances from the first to all points in the neighborhood
96 * \param[in] max_dist the upper bound for the considered distance interval
97 * \param[in] nr_subdiv the number of subdivisions for the considered distance interval
98 * \param[in] plane_radius maximum radius, above which everything can be considered planar
99 * \param[in] radii the output point of a type that should have r_min and r_max fields
100 * \param[in] compute_histogram if not false, the full neighborhood histogram is provided, usable as a point signature
101 * \ingroup features
102 */
103 template <typename PointNT, typename PointOutT> Eigen::MatrixXf
104 computeRSD (const pcl::PointCloud<PointNT> &normals,
105 const pcl::Indices &indices, const std::vector<float> &sqr_dists, double max_dist,
106 int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram = false);
107
108 /** \brief @b RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves)
109 * for a given point cloud dataset containing points and normals.
110 *
111 * @note If you use this code in any academic work, please cite:
112 *
113 * <ul>
114 * <li> Z.C. Marton , D. Pangercic , N. Blodow , J. Kleinehellefort, M. Beetz
115 * General 3D Modelling of Novel Objects from a Single View
116 * In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
117 * Taipei, Taiwan, October 18-22, 2010
118 * </li>
119 * <li> Z.C. Marton, D. Pangercic, N. Blodow, Michael Beetz.
120 * Combined 2D-3D Categorization and Classification for Multimodal Perception Systems.
121 * In The International Journal of Robotics Research, Sage Publications
122 * pages 1378--1402, Volume 30, Number 11, September 2011.
123 * </li>
124 * </ul>
125 *
126 * @note The code is stateful as we do not expect this class to be multicore parallelized.
127 * \author Zoltan-Csaba Marton
128 * \ingroup features
129 */
130 template <typename PointInT, typename PointNT, typename PointOutT>
131 class RSDEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
132 {
133 public:
134 using Feature<PointInT, PointOutT>::feature_name_;
135 using Feature<PointInT, PointOutT>::getClassName;
136 using Feature<PointInT, PointOutT>::indices_;
137 using Feature<PointInT, PointOutT>::search_radius_;
138 using Feature<PointInT, PointOutT>::search_parameter_;
139 using Feature<PointInT, PointOutT>::surface_;
140 using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
141
144
145 using Ptr = shared_ptr<RSDEstimation<PointInT, PointNT, PointOutT> >;
146 using ConstPtr = shared_ptr<const RSDEstimation<PointInT, PointNT, PointOutT> >;
147
148
149 /** \brief Empty constructor. */
150 RSDEstimation () : nr_subdiv_ (5), plane_radius_ (0.2), save_histograms_ (false)
151 {
152 feature_name_ = "RadiusSurfaceDescriptor";
153 };
154
155 /** \brief Set the number of subdivisions for the considered distance interval.
156 * \param[in] nr_subdiv the number of subdivisions
157 */
158 inline void
159 setNrSubdivisions (int nr_subdiv) { nr_subdiv_ = nr_subdiv; }
160
161 /** \brief Get the number of subdivisions for the considered distance interval.
162 * \return the number of subdivisions
163 */
164 inline int
165 getNrSubdivisions () const { return (nr_subdiv_); }
166
167 /** \brief Set the maximum radius, above which everything can be considered planar.
168 * \note the order of magnitude should be around 10-20 times the search radius (0.2 works well for typical datasets).
169 * \note on accurate 3D data (e.g. openni sernsors) a search radius as low as 0.01 still gives good results.
170 * \param[in] plane_radius the new plane radius
171 */
172 inline void
173 setPlaneRadius (double plane_radius) { plane_radius_ = plane_radius; }
174
175 /** \brief Get the maximum radius, above which everything can be considered planar.
176 * \return the plane_radius used
177 */
178 inline double
179 getPlaneRadius () const { return (plane_radius_); }
180
181 /** \brief Disables the setting of the number of k nearest neighbors to use for the feature estimation. */
182 inline void
184 {
185 PCL_ERROR ("[pcl::%s::setKSearch] RSD does not work with k nearest neighbor search. Use setRadiusSearch() instead!\n", getClassName ().c_str ());
186 }
187
188 /** \brief Set whether the full distance-angle histograms should be saved.
189 * @note Obtain the list of histograms by getHistograms ()
190 * \param[in] save set to true if histograms should be saved
191 */
192 inline void
193 setSaveHistograms (bool save) { save_histograms_ = save; }
194
195 /** \brief Returns whether the full distance-angle histograms are being saved.
196 * \return true if the histograms are being be saved
197 */
198 inline bool
199 getSaveHistograms () const { return (save_histograms_); }
200
201 /** \brief Returns a pointer to the list of full distance-angle histograms for all points.
202 * \return the histogram being saved when computing RSD
203 */
204 inline shared_ptr<std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > >
205 getHistograms () const { return (histograms_); }
206
207 protected:
208
209 /** \brief Estimate the estimates the Radius-based Surface Descriptor (RSD) at a set of points given by
210 * <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in
211 * setSearchMethod ()
212 * \param output the resultant point cloud model dataset that contains the RSD feature estimates (r_min and r_max values)
213 */
214 void
215 computeFeature (PointCloudOut &output) override;
216
217 /** \brief The list of full distance-angle histograms for all points. */
218 shared_ptr<std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > > histograms_;
219
220 private:
221 /** \brief The number of subdivisions for the considered distance interval. */
222 int nr_subdiv_;
223
224 /** \brief The maximum radius, above which everything can be considered planar. */
225 double plane_radius_;
226
227 /** \brief Signals whether the full distance-angle histograms are being saved. */
228 bool save_histograms_;
229
230 public:
232 };
233}
234
235#ifdef PCL_NO_PRECOMPILE
236#include <pcl/features/impl/rsd.hpp>
237#endif
PointCloudNConstPtr normals_
A pointer to the input dataset that contains the point normals of the XYZ dataset.
Definition feature.h:355
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:237
const std::string & getClassName() const
Get a string representation of the name of this class.
Definition feature.h:247
double search_radius_
The nearest neighbors search radius for each point.
Definition feature.h:240
std::string feature_name_
The feature name.
Definition feature.h:223
PointCloudInConstPtr surface_
An input point cloud describing the surface that is to be used for nearest neighbors estimation.
Definition feature.h:231
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition pcl_base.h:150
PointCloud represents the base class in PCL for storing collections of 3D points.
iterator begin() noexcept
RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local ...
Definition rsd.h:132
void setNrSubdivisions(int nr_subdiv)
Set the number of subdivisions for the considered distance interval.
Definition rsd.h:159
shared_ptr< std::vector< Eigen::MatrixXf, Eigen::aligned_allocator< Eigen::MatrixXf > > > getHistograms() const
Returns a pointer to the list of full distance-angle histograms for all points.
Definition rsd.h:205
shared_ptr< const RSDEstimation< PointInT, PointNT, PointOutT > > ConstPtr
Definition rsd.h:146
void setKSearch(int)
Disables the setting of the number of k nearest neighbors to use for the feature estimation.
Definition rsd.h:183
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition rsd.h:142
int getNrSubdivisions() const
Get the number of subdivisions for the considered distance interval.
Definition rsd.h:165
typename Feature< PointInT, PointOutT >::PointCloudIn PointCloudIn
Definition rsd.h:143
void computeFeature(PointCloudOut &output) override
Estimate the estimates the Radius-based Surface Descriptor (RSD) at a set of points given by <setInpu...
Definition rsd.hpp:248
RSDEstimation()
Empty constructor.
Definition rsd.h:150
bool getSaveHistograms() const
Returns whether the full distance-angle histograms are being saved.
Definition rsd.h:199
double getPlaneRadius() const
Get the maximum radius, above which everything can be considered planar.
Definition rsd.h:179
shared_ptr< RSDEstimation< PointInT, PointNT, PointOutT > > Ptr
Definition rsd.h:145
void setSaveHistograms(bool save)
Set whether the full distance-angle histograms should be saved.
Definition rsd.h:193
shared_ptr< std::vector< Eigen::MatrixXf, Eigen::aligned_allocator< Eigen::MatrixXf > > > histograms_
The list of full distance-angle histograms for all points.
Definition rsd.h:218
void setPlaneRadius(double plane_radius)
Set the maximum radius, above which everything can be considered planar.
Definition rsd.h:173
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition memory.h:63
void getFeaturePointCloud(const std::vector< Eigen::MatrixXf, Eigen::aligned_allocator< Eigen::MatrixXf > > &histograms2D, PointCloud< Histogram< N > > &histogramsPC)
Transform a list of 2D matrices into a point cloud containing the values in a vector (Histogram<N>).
Definition rsd.h:57
Eigen::MatrixXf computeRSD(const pcl::PointCloud< PointInT > &surface, const pcl::PointCloud< PointNT > &normals, const pcl::Indices &indices, double max_dist, int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram=false)
Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhoo...
Definition rsd.hpp:49
Defines functions, macros and traits for allocating and using memory.
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
Defines all the PCL and non-PCL macros used.
A point structure representing an N-D histogram.