Point Cloud Library (PCL) 1.15.0
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pfh.h
1/*
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40
41#pragma once
42
43#include <pcl/point_types.h>
44#include <pcl/features/feature.h>
45#include <map>
46#include <queue> // for std::queue
47
48namespace pcl
49{
50 /** \brief PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset
51 * containing points and normals.
52 *
53 * A commonly used type for PointOutT is pcl::PFHSignature125.
54 *
55 * \note If you use this code in any academic work, please cite:
56 *
57 * - R.B. Rusu, N. Blodow, Z.C. Marton, M. Beetz.
58 * Aligning Point Cloud Views using Persistent Feature Histograms.
59 * In Proceedings of the 21st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
60 * Nice, France, September 22-26 2008.
61 * - R.B. Rusu, Z.C. Marton, N. Blodow, M. Beetz.
62 * Learning Informative Point Classes for the Acquisition of Object Model Maps.
63 * In Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV),
64 * Hanoi, Vietnam, December 17-20 2008.
65 *
66 * \attention
67 * The convention for PFH features is:
68 * - if a query point's nearest neighbors cannot be estimated, the PFH feature will be set to NaN
69 * (not a number)
70 * - it is impossible to estimate a PFH descriptor for a point that
71 * doesn't have finite 3D coordinates. Therefore, any point that contains
72 * NaN data on x, y, or z, will have its PFH feature property set to NaN.
73 *
74 * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
75 * \ref FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram).
76 *
77 * \author Radu B. Rusu
78 * \ingroup features
79 */
80 template <typename PointInT, typename PointNT, typename PointOutT = pcl::PFHSignature125>
81 class PFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
82 {
83 public:
84 using Ptr = shared_ptr<PFHEstimation<PointInT, PointNT, PointOutT> >;
85 using ConstPtr = shared_ptr<const PFHEstimation<PointInT, PointNT, PointOutT> >;
86 using Feature<PointInT, PointOutT>::feature_name_;
87 using Feature<PointInT, PointOutT>::getClassName;
88 using Feature<PointInT, PointOutT>::indices_;
89 using Feature<PointInT, PointOutT>::k_;
90 using Feature<PointInT, PointOutT>::search_parameter_;
91 using Feature<PointInT, PointOutT>::surface_;
92 using Feature<PointInT, PointOutT>::input_;
93 using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
94
97
98 /** \brief Empty constructor.
99 * Sets \a use_cache_ to false, \a nr_subdiv_ to 5, and the internal maximum cache size to 1GB.
100 */
102
103 d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI))),
104 key_list_ (),
105 // Default 1GB memory size. Need to set it to something more conservative.
106 max_cache_size_ ((1ul*1024ul*1024ul*1024ul) / sizeof (std::pair<std::pair<int, int>, Eigen::Vector4f>))
107 {
108 feature_name_ = "PFHEstimation";
109 }
110
111 /** \brief Set the maximum internal cache size. Defaults to 2GB worth of entries.
112 * \param[in] cache_size maximum cache size
113 */
114 inline void
115 setMaximumCacheSize (unsigned int cache_size)
116 {
117 max_cache_size_ = cache_size;
118 }
119
120 /** \brief Get the maximum internal cache size. */
121 inline unsigned int
123 {
124 return (max_cache_size_);
125 }
126
127 /** \brief Set whether to use an internal cache mechanism for removing redundant calculations or not.
128 *
129 * \note Depending on how the point cloud is ordered and how the nearest
130 * neighbors are estimated, using a cache could have a positive or a
131 * negative influence. Please test with and without a cache on your
132 * data, and choose whatever works best!
133 *
134 * See \ref setMaximumCacheSize for setting the maximum cache size
135 *
136 * \param[in] use_cache set to true to use the internal cache, false otherwise
137 */
138 inline void
139 setUseInternalCache (bool use_cache)
140 {
141 use_cache_ = use_cache;
142 }
143
144 /** \brief Get whether the internal cache is used or not for computing the PFH features. */
145 inline bool
147 {
148 return (use_cache_);
149 }
150
151 /** \brief Compute the 4-tuple representation containing the three angles and one distance between two points
152 * represented by Cartesian coordinates and normals.
153 * \note For explanations about the features, please see the literature mentioned above (the order of the
154 * features might be different).
155 * \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
156 * \param[in] normals the dataset containing the surface normals (assuming normalized vectors) at each point in cloud
157 * \param[in] p_idx the index of the first point (source)
158 * \param[in] q_idx the index of the second point (target)
159 * \param[out] f1 the first angular feature (angle between the projection of nq_idx and u)
160 * \param[out] f2 the second angular feature (angle between nq_idx and v)
161 * \param[out] f3 the third angular feature (angle between np_idx and |p_idx - q_idx|)
162 * \param[out] f4 the distance feature (p_idx - q_idx)
163 * \note For efficiency reasons, we assume that the point data passed to the method is finite.
164 */
165 bool
167 int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4);
168
169 /** \brief Estimate the PFH (Point Feature Histograms) individual signatures of the three angular (f1, f2, f3)
170 * features for a given point based on its spatial neighborhood of 3D points with normals
171 * \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
172 * \param[in] normals the dataset containing the surface normals at each point in \a cloud
173 * \param[in] indices the k-neighborhood point indices in the dataset
174 * \param[in] nr_split the number of subdivisions for each angular feature interval
175 * \param[out] pfh_histogram the resultant (combinatorial) PFH histogram representing the feature at the query point
176 */
177 void
179 const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram);
180
181 protected:
182 /** \brief Estimate the Point Feature Histograms (PFH) descriptors at a set of points given by
183 * <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in
184 * setSearchMethod ()
185 * \param[out] output the resultant point cloud model dataset that contains the PFH feature estimates
186 */
187 void
188 computeFeature (PointCloudOut &output) override;
189
190 /** \brief The number of subdivisions for each angular feature interval. */
192
193 /** \brief Placeholder for a point's PFH signature. */
194 Eigen::VectorXf pfh_histogram_;
195
196 /** \brief Placeholder for a PFH 4-tuple. */
197 Eigen::Vector4f pfh_tuple_;
198
199 /** \brief Placeholder for a histogram index. */
200 int f_index_[3];
201
202 /** \brief Float constant = 1.0 / (2.0 * M_PI) */
203 float d_pi_;
204
205 /** \brief Internal hashmap, used to optimize efficiency of redundant computations. */
206 std::map<std::pair<int, int>, Eigen::Vector4f, std::less<>, Eigen::aligned_allocator<std::pair<const std::pair<int, int>, Eigen::Vector4f> > > feature_map_;
207
208 /** \brief Queue of pairs saved, used to constrain memory usage. */
209 std::queue<std::pair<int, int> > key_list_;
210
211 /** \brief Maximum size of internal cache memory. */
212 unsigned int max_cache_size_;
213
214 /** \brief Set to true to use the internal cache for removing redundant computations. */
215 bool use_cache_{false};
216 };
217}
218
219#ifdef PCL_NO_PRECOMPILE
220#include <pcl/features/impl/pfh.hpp>
221#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
PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset ...
Definition pfh.h:82
void computePointPFHSignature(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram)
Estimate the PFH (Point Feature Histograms) individual signatures of the three angular (f1,...
Definition pfh.hpp:61
bool computePairFeatures(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
Definition pfh.hpp:49
float d_pi_
Float constant = 1.0 / (2.0 * M_PI)
Definition pfh.h:203
int f_index_[3]
Placeholder for a histogram index.
Definition pfh.h:200
shared_ptr< PFHEstimation< PointInT, PointNT, PointOutT > > Ptr
Definition pfh.h:84
void computeFeature(PointCloudOut &output) override
Estimate the Point Feature Histograms (PFH) descriptors at a set of points given by <setInputCloud ()...
Definition pfh.hpp:167
Eigen::VectorXf pfh_histogram_
Placeholder for a point's PFH signature.
Definition pfh.h:194
Eigen::Vector4f pfh_tuple_
Placeholder for a PFH 4-tuple.
Definition pfh.h:197
PFHEstimation()
Empty constructor.
Definition pfh.h:101
typename Feature< PointInT, PointOutT >::PointCloudIn PointCloudIn
Definition pfh.h:96
shared_ptr< const PFHEstimation< PointInT, PointNT, PointOutT > > ConstPtr
Definition pfh.h:85
int nr_subdiv_
The number of subdivisions for each angular feature interval.
Definition pfh.h:191
void setUseInternalCache(bool use_cache)
Set whether to use an internal cache mechanism for removing redundant calculations or not.
Definition pfh.h:139
bool getUseInternalCache()
Get whether the internal cache is used or not for computing the PFH features.
Definition pfh.h:146
bool use_cache_
Set to true to use the internal cache for removing redundant computations.
Definition pfh.h:215
unsigned int max_cache_size_
Maximum size of internal cache memory.
Definition pfh.h:212
void setMaximumCacheSize(unsigned int cache_size)
Set the maximum internal cache size.
Definition pfh.h:115
std::queue< std::pair< int, int > > key_list_
Queue of pairs saved, used to constrain memory usage.
Definition pfh.h:209
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition pfh.h:95
std::map< std::pair< int, int >, Eigen::Vector4f, std::less<>, Eigen::aligned_allocator< std::pair< const std::pair< int, int >, Eigen::Vector4f > > > feature_map_
Internal hashmap, used to optimize efficiency of redundant computations.
Definition pfh.h:206
unsigned int getMaximumCacheSize()
Get the maximum internal cache size.
Definition pfh.h:122
Defines all the PCL implemented PointT point type structures.
Definition bfgs.h:10
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
#define M_PI
Definition pcl_macros.h:203