Point Cloud Library (PCL)  1.9.1-dev
hough_3d.h
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39 
40 #pragma once
41 
42 #include <pcl/recognition/cg/correspondence_grouping.h>
43 #include <pcl/recognition/boost.h>
44 #include <pcl/point_types.h>
45 
46 namespace pcl
47 {
48  namespace recognition
49  {
50  /** \brief HoughSpace3D is a 3D voting space. Cast votes can be interpolated in order to better deal with approximations introduced by bin quantization. A weight can also be associated with each vote.
51  * \author Federico Tombari (original), Tommaso Cavallari (PCL port)
52  * \ingroup recognition
53  */
54  class PCL_EXPORTS HoughSpace3D
55  {
56 
57  public:
58 
59  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
60 
61  typedef boost::shared_ptr<HoughSpace3D> Ptr;
62 
63  /** \brief Constructor
64  *
65  * \param[in] min_coord minimum (x,y,z) coordinates of the Hough space
66  * \param[in] bin_size size of each bing of the Hough space.
67  * \param[in] max_coord maximum (x,y,z) coordinates of the Hough space.
68  */
69  HoughSpace3D (const Eigen::Vector3d &min_coord, const Eigen::Vector3d &bin_size, const Eigen::Vector3d &max_coord);
70 
71  /** \brief Reset all cast votes. */
72  void
73  reset ();
74 
75  /** \brief Casting a vote for a given position in the Hough space.
76  *
77  * \param[in] single_vote_coord coordinates of the vote being cast (in absolute coordinates)
78  * \param[in] weight weight associated with the vote.
79  * \param[in] voter_id the numeric id of the voter. Useful to trace back the voting correspondence, if the vote is returned by findMaxima as part of a maximum of the Hough Space.
80  * \return the index of the bin in which the vote has been cast.
81  */
82  int
83  vote (const Eigen::Vector3d &single_vote_coord, double weight, int voter_id);
84 
85  /** \brief Vote for a given position in the 3D space. The weight is interpolated between the bin pointed by single_vote_coord and its neighbors.
86  *
87  * \param[in] single_vote_coord coordinates of the vote being cast.
88  * \param[in] weight weight associated with the vote.
89  * \param[in] voter_id the numeric id of the voter. Useful to trace back the voting correspondence, if the vote is returned by findMaxima as a part of a maximum of the Hough Space.
90  * \return the index of the bin in which the vote has been cast.
91  */
92  int
93  voteInt (const Eigen::Vector3d &single_vote_coord, double weight, int voter_id);
94 
95  /** \brief Find the bins with most votes.
96  *
97  * \param[in] min_threshold the minimum number of votes to be included in a bin in order to have its value returned.
98  * If set to a value between -1 and 0 the Hough space maximum_vote is found and the returned values are all the votes greater than -min_threshold * maximum_vote.
99  * \param[out] maxima_values the list of Hough Space bin values greater than min_threshold.
100  * \param[out] maxima_voter_ids for each value returned, a list of the voter ids who cast a vote in that position.
101  * \return The min_threshold used, either set by the user or found by this method.
102  */
103  double
104  findMaxima (double min_threshold, std::vector<double> & maxima_values, std::vector<std::vector<int> > &maxima_voter_ids);
105 
106  protected:
107 
108  /** \brief Minimum coordinate in the Hough Space. */
109  Eigen::Vector3d min_coord_;
110 
111  /** \brief Size of each bin in the Hough Space. */
112  Eigen::Vector3d bin_size_;
113 
114  /** \brief Number of bins for each dimension. */
115  Eigen::Vector3i bin_count_;
116 
117  /** \brief Used to access hough_space_ as if it was a matrix. */
118  int partial_bin_products_[4];
119 
120  /** \brief Total number of bins in the Hough Space. */
122 
123  /** \brief The Hough Space. */
124  std::vector<double> hough_space_;
125  //boost::unordered_map<int, double> hough_space_;
126 
127  /** \brief List of voters for each bin. */
128  boost::unordered_map<int, std::vector<int> > voter_ids_;
129  };
130  }
131 
132  /** \brief Class implementing a 3D correspondence grouping algorithm that can deal with multiple instances of a model template
133  * found into a given scene. Each correspondence casts a vote for a reference point in a 3D Hough Space.
134  * The remaining 3 DOF are taken into account by associating each correspondence with a local Reference Frame.
135  * The suggested PointModelRfT is pcl::ReferenceFrame
136  *
137  * \note If you use this code in any academic work, please cite the original paper:
138  * - F. Tombari, L. Di Stefano:
139  * Object recognition in 3D scenes with occlusions and clutter by Hough voting.
140  * 2010, Fourth Pacific-Rim Symposium on Image and Video Technology
141  *
142  * \author Federico Tombari (original), Tommaso Cavallari (PCL port)
143  * \ingroup recognition
144  */
145  template<typename PointModelT, typename PointSceneT, typename PointModelRfT = pcl::ReferenceFrame, typename PointSceneRfT = pcl::ReferenceFrame>
146  class Hough3DGrouping : public CorrespondenceGrouping<PointModelT, PointSceneT>
147  {
148  public:
152 
156 
158  typedef typename PointCloud::Ptr PointCloudPtr;
160 
162 
163  /** \brief Constructor */
165  : input_rf_ ()
166  , scene_rf_ ()
167  , needs_training_ (true)
168  , model_votes_ ()
169  , hough_threshold_ (-1)
170  , hough_bin_size_ (1.0)
171  , use_interpolation_ (true)
172  , use_distance_weight_ (false)
173  , local_rf_normals_search_radius_ (0.0f)
174  , local_rf_search_radius_ (0.0f)
175  , hough_space_ ()
176  , found_transformations_ ()
177  , hough_space_initialized_ (false)
178  {}
179 
180  /** \brief Provide a pointer to the input dataset.
181  * \param[in] cloud the const boost shared pointer to a PointCloud message.
182  */
183  inline void
184  setInputCloud (const PointCloudConstPtr &cloud) override
185  {
187  needs_training_ = true;
188  hough_space_initialized_ = false;
189  input_rf_.reset();
190  }
191 
192  /** \brief Provide a pointer to the input dataset's reference frames.
193  * Each point in the reference frame cloud should be the reference frame of
194  * the correspondent point in the input dataset.
195  *
196  * \param[in] input_rf the pointer to the input cloud's reference frames.
197  */
198  inline void
199  setInputRf (const ModelRfCloudConstPtr &input_rf)
200  {
201  input_rf_ = input_rf;
202  needs_training_ = true;
203  hough_space_initialized_ = false;
204  }
205 
206  /** \brief Getter for the input dataset's reference frames.
207  * Each point in the reference frame cloud should be the reference frame of
208  * the correspondent point in the input dataset.
209  *
210  * \return the pointer to the input cloud's reference frames.
211  */
212  inline ModelRfCloudConstPtr
213  getInputRf () const
214  {
215  return (input_rf_);
216  }
217 
218  /** \brief Provide a pointer to the scene dataset (i.e. the cloud in which the algorithm has to search for instances of the input model)
219  *
220  * \param[in] scene the const boost shared pointer to a PointCloud message.
221  */
222  inline void
223  setSceneCloud (const SceneCloudConstPtr &scene) override
224  {
225  scene_ = scene;
226  hough_space_initialized_ = false;
227  scene_rf_.reset();
228  }
229 
230  /** \brief Provide a pointer to the scene dataset's reference frames.
231  * Each point in the reference frame cloud should be the reference frame of
232  * the correspondent point in the scene dataset.
233  *
234  * \param[in] scene_rf the pointer to the scene cloud's reference frames.
235  */
236  inline void
237  setSceneRf (const SceneRfCloudConstPtr &scene_rf)
238  {
239  scene_rf_ = scene_rf;
240  hough_space_initialized_ = false;
241  }
242 
243  /** \brief Getter for the scene dataset's reference frames.
244  * Each point in the reference frame cloud should be the reference frame of
245  * the correspondent point in the scene dataset.
246  *
247  * \return the pointer to the scene cloud's reference frames.
248  */
249  inline SceneRfCloudConstPtr
250  getSceneRf () const
251  {
252  return (scene_rf_);
253  }
254 
255  /** \brief Provide a pointer to the precomputed correspondences between points in the input dataset and
256  * points in the scene dataset. The correspondences are going to be clustered into different model instances
257  * by the algorithm.
258  *
259  * \param[in] corrs the correspondences between the model and the scene.
260  */
261  inline void
263  {
264  model_scene_corrs_ = corrs;
265  hough_space_initialized_ = false;
266  }
267 
268  /** \brief Sets the minimum number of votes in the Hough space needed to infer the presence of a model instance into the scene cloud.
269  *
270  * \param[in] threshold the threshold for the Hough space voting, if set between -1 and 0 the maximum vote in the
271  * entire space is automatically calculated and -threshold the maximum value is used as a threshold. This means
272  * that a value between -1 and 0 should be used only if at least one instance of the model is always present in
273  * the scene, or if this false positive can be filtered later.
274  */
275  inline void
276  setHoughThreshold (double threshold)
277  {
278  hough_threshold_ = threshold;
279  }
280 
281  /** \brief Gets the minimum number of votes in the Hough space needed to infer the presence of a model instance into the scene cloud.
282  *
283  * \return the threshold for the Hough space voting.
284  */
285  inline double
287  {
288  return (hough_threshold_);
289  }
290 
291  /** \brief Sets the size of each bin into the Hough space.
292  *
293  * \param[in] bin_size the size of each Hough space's bin.
294  */
295  inline void
296  setHoughBinSize (double bin_size)
297  {
298  hough_bin_size_ = bin_size;
299  hough_space_initialized_ = false;
300  }
301 
302  /** \brief Gets the size of each bin into the Hough space.
303  *
304  * \return the size of each Hough space's bin.
305  */
306  inline double
308  {
309  return (hough_bin_size_);
310  }
311 
312  /** \brief Sets whether the vote casting procedure interpolates
313  * the score between neighboring bins of the Hough space or not.
314  *
315  * \param[in] use_interpolation the algorithm should interpolate the vote score between neighboring bins.
316  */
317  inline void
318  setUseInterpolation (bool use_interpolation)
319  {
320  use_interpolation_ = use_interpolation;
321  hough_space_initialized_ = false;
322  }
323 
324  /** \brief Gets whether the vote casting procedure interpolates
325  * the score between neighboring bins of the Hough space or not.
326  *
327  * \return if the algorithm should interpolate the vote score between neighboring bins.
328  */
329  inline bool
331  {
332  return (use_interpolation_);
333  }
334 
335  /** \brief Sets whether the vote casting procedure uses the correspondence's distance as a score.
336  *
337  * \param[in] use_distance_weight the algorithm should use the weighted distance when calculating the Hough voting score.
338  */
339  inline void
340  setUseDistanceWeight (bool use_distance_weight)
341  {
342  use_distance_weight_ = use_distance_weight;
343  hough_space_initialized_ = false;
344  }
345 
346  /** \brief Gets whether the vote casting procedure uses the correspondence's distance as a score.
347  *
348  * \return if the algorithm should use the weighted distance when calculating the Hough voting score.
349  */
350  inline bool
352  {
353  return (use_distance_weight_);
354  }
355 
356  /** \brief If the Local reference frame has not been set for either the model cloud or the scene cloud,
357  * this algorithm makes the computation itself but needs a suitable search radius to compute the normals
358  * in order to subsequently compute the RF (if not set a default 15 nearest neighbors search is performed).
359  *
360  * \param[in] local_rf_normals_search_radius the normals search radius for the local reference frame calculation.
361  */
362  inline void
363  setLocalRfNormalsSearchRadius (float local_rf_normals_search_radius)
364  {
365  local_rf_normals_search_radius_ = local_rf_normals_search_radius;
366  needs_training_ = true;
367  hough_space_initialized_ = false;
368  }
369 
370  /** \brief If the Local reference frame has not been set for either the model cloud or the scene cloud,
371  * this algorithm makes the computation itself but needs a suitable search radius to compute the normals
372  * in order to subsequently compute the RF (if not set a default 15 nearest neighbors search is performed).
373  *
374  * \return the normals search radius for the local reference frame calculation.
375  */
376  inline float
378  {
379  return (local_rf_normals_search_radius_);
380  }
381 
382  /** \brief If the Local reference frame has not been set for either the model cloud or the scene cloud,
383  * this algorithm makes the computation itself but needs a suitable search radius to do so.
384  * \attention This parameter NEEDS to be set if the reference frames are not precomputed externally,
385  * otherwise the recognition results won't be correct.
386  *
387  * \param[in] local_rf_search_radius the search radius for the local reference frame calculation.
388  */
389  inline void
390  setLocalRfSearchRadius (float local_rf_search_radius)
391  {
392  local_rf_search_radius_ = local_rf_search_radius;
393  needs_training_ = true;
394  hough_space_initialized_ = false;
395  }
396 
397  /** \brief If the Local reference frame has not been set for either the model cloud or the scene cloud,
398  * this algorithm makes the computation itself but needs a suitable search radius to do so.
399  * \attention This parameter NEEDS to be set if the reference frames are not precomputed externally,
400  * otherwise the recognition results won't be correct.
401  *
402  * \return the search radius for the local reference frame calculation.
403  */
404  inline float
406  {
407  return (local_rf_search_radius_);
408  }
409 
410  /** \brief Call this function after setting the input, the input_rf and the hough_bin_size parameters to perform an off line training of the algorithm. This might be useful if one wants to perform once and for all a pre-computation of votes that only concern the models, increasing the on-line efficiency of the grouping algorithm.
411  * The algorithm is automatically trained on the first invocation of the recognize method or the cluster method if this training function has not been manually invoked.
412  *
413  * \return true if the training had been successful or false if errors have occurred.
414  */
415  bool
416  train ();
417 
418  /** \brief The main function, recognizes instances of the model into the scene set by the user.
419  *
420  * \param[out] transformations a vector containing one transformation matrix for each instance of the model recognized into the scene.
421  *
422  * \return true if the recognition had been successful or false if errors have occurred.
423  */
424  bool
425  recognize (std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > &transformations);
426 
427  /** \brief The main function, recognizes instances of the model into the scene set by the user.
428  *
429  * \param[out] transformations a vector containing one transformation matrix for each instance of the model recognized into the scene.
430  * \param[out] clustered_corrs a vector containing the correspondences for each instance of the model found within the input data (the same output of clusterCorrespondences).
431  *
432  * \return true if the recognition had been successful or false if errors have occurred.
433  */
434  bool
435  recognize (std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > &transformations, std::vector<pcl::Correspondences> &clustered_corrs);
436 
437  protected:
441 
442  /** \brief The input Rf cloud. */
443  ModelRfCloudConstPtr input_rf_;
444 
445  /** \brief The scene Rf cloud. */
446  SceneRfCloudConstPtr scene_rf_;
447 
448  /** \brief If the training of the Hough space is needed; set on change of either the input cloud or the input_rf. */
450 
451  /** \brief The result of the training. The vector between each model point and the centroid of the model adjusted by its local reference frame.*/
452  std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > model_votes_;
453 
454  /** \brief The minimum number of votes in the Hough space needed to infer the presence of a model instance into the scene cloud. */
456 
457  /** \brief The size of each bin of the hough space. */
459 
460  /** \brief Use the interpolation between neighboring Hough bins when casting votes. */
462 
463  /** \brief Use the weighted correspondence distance when casting votes. */
465 
466  /** \brief Normals search radius for the potential Rf calculation. */
468 
469  /** \brief Search radius for the potential Rf calculation. */
471 
472  /** \brief The Hough space. */
474 
475  /** \brief Transformations found by clusterCorrespondences method. */
476  std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > found_transformations_;
477 
478  /** \brief Whether the Hough space already contains the correct votes for the current input parameters and so the cluster and recognize calls don't need to recompute each value.
479  * Reset on the change of any parameter except the hough_threshold.
480  */
482 
483  /** \brief Cluster the input correspondences in order to distinguish between different instances of the model into the scene.
484  *
485  * \param[out] model_instances a vector containing the clustered correspondences for each model found on the scene.
486  * \return true if the clustering had been successful or false if errors have occurred.
487  */
488  void
489  clusterCorrespondences (std::vector<Correspondences> &model_instances) override;
490 
491  /* \brief Finds the transformation matrix between the input and the scene cloud for a set of correspondences using a RANSAC algorithm.
492  * \param[in] the scene cloud in which the PointSceneT has been converted to PointModelT.
493  * \param[in] corrs a set of correspondences.
494  * \param[out] transform the transformation matrix between the input cloud and the scene cloud that aligns the found correspondences.
495  * \return true if the recognition had been successful or false if errors have occurred.
496  */
497  //bool
498  //getTransformMatrix (const PointCloudConstPtr &scene_cloud, const Correspondences &corrs, Eigen::Matrix4f &transform);
499 
500  /** \brief The Hough space voting procedure.
501  * \return true if the voting had been successful or false if errors have occurred.
502  */
503  bool
504  houghVoting ();
505 
506  /** \brief Computes the reference frame for an input cloud.
507  * \param[in] input the input cloud.
508  * \param[out] rf the resulting reference frame.
509  */
510  template<typename PointType, typename PointRfType> void
511  computeRf (const typename pcl::PointCloud<PointType>::ConstPtr &input, pcl::PointCloud<PointRfType> &rf);
512  };
513 }
514 
515 #ifdef PCL_NO_PRECOMPILE
516 #include <pcl/recognition/impl/cg/hough_3d.hpp>
517 #endif
boost::unordered_map< int, std::vector< int > > voter_ids_
List of voters for each bin.
Definition: hough_3d.h:128
Eigen::Vector3i bin_count_
Number of bins for each dimension.
Definition: hough_3d.h:115
ModelRfCloud::ConstPtr ModelRfCloudConstPtr
Definition: hough_3d.h:151
std::vector< double > hough_space_
The Hough Space.
Definition: hough_3d.h:124
float getLocalRfSearchRadius() const
If the Local reference frame has not been set for either the model cloud or the scene cloud...
Definition: hough_3d.h:405
pcl::PointCloud< PointSceneRfT > SceneRfCloud
Definition: hough_3d.h:153
float getLocalRfNormalsSearchRadius() const
If the Local reference frame has not been set for either the model cloud or the scene cloud...
Definition: hough_3d.h:377
int total_bins_count_
Total number of bins in the Hough Space.
Definition: hough_3d.h:121
This file defines compatibility wrappers for low level I/O functions.
Definition: convolution.h:44
void setUseDistanceWeight(bool use_distance_weight)
Sets whether the vote casting procedure uses the correspondence&#39;s distance as a score.
Definition: hough_3d.h:340
SceneRfCloud::Ptr SceneRfCloudPtr
Definition: hough_3d.h:154
SceneRfCloudConstPtr scene_rf_
The scene Rf cloud.
Definition: hough_3d.h:446
pcl::PointCloud< PointModelT > PointCloud
Definition: hough_3d.h:157
double getHoughThreshold() const
Gets the minimum number of votes in the Hough space needed to infer the presence of a model instance ...
Definition: hough_3d.h:286
SceneRfCloud::ConstPtr SceneRfCloudConstPtr
Definition: hough_3d.h:155
void setLocalRfNormalsSearchRadius(float local_rf_normals_search_radius)
If the Local reference frame has not been set for either the model cloud or the scene cloud...
Definition: hough_3d.h:363
SceneRfCloudConstPtr getSceneRf() const
Getter for the scene dataset&#39;s reference frames.
Definition: hough_3d.h:250
float local_rf_normals_search_radius_
Normals search radius for the potential Rf calculation.
Definition: hough_3d.h:467
bool hough_space_initialized_
Whether the Hough space already contains the correct votes for the current input parameters and so th...
Definition: hough_3d.h:481
Eigen::Vector3d bin_size_
Size of each bin in the Hough Space.
Definition: hough_3d.h:112
pcl::recognition::HoughSpace3D::Ptr hough_space_
The Hough space.
Definition: hough_3d.h:473
boost::shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:427
bool use_distance_weight_
Use the weighted correspondence distance when casting votes.
Definition: hough_3d.h:464
bool getUseDistanceWeight() const
Gets whether the vote casting procedure uses the correspondence&#39;s distance as a score.
Definition: hough_3d.h:351
void setInputRf(const ModelRfCloudConstPtr &input_rf)
Provide a pointer to the input dataset&#39;s reference frames.
Definition: hough_3d.h:199
PointCloud::ConstPtr PointCloudConstPtr
Definition: hough_3d.h:159
bool needs_training_
If the training of the Hough space is needed; set on change of either the input cloud or the input_rf...
Definition: hough_3d.h:449
boost::shared_ptr< const Correspondences > CorrespondencesConstPtr
void setSceneCloud(const SceneCloudConstPtr &scene) override
Provide a pointer to the scene dataset (i.e.
Definition: hough_3d.h:223
ModelRfCloud::Ptr ModelRfCloudPtr
Definition: hough_3d.h:150
void setModelSceneCorrespondences(const CorrespondencesConstPtr &corrs) override
Provide a pointer to the precomputed correspondences between points in the input dataset and points i...
Definition: hough_3d.h:262
Hough3DGrouping()
Constructor.
Definition: hough_3d.h:164
EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef boost::shared_ptr< HoughSpace3D > Ptr
Definition: hough_3d.h:61
ModelRfCloudConstPtr input_rf_
The input Rf cloud.
Definition: hough_3d.h:443
boost::shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:428
pcl::CorrespondenceGrouping< PointModelT, PointSceneT >::SceneCloudConstPtr SceneCloudConstPtr
Definition: hough_3d.h:161
bool use_interpolation_
Use the interpolation between neighboring Hough bins when casting votes.
Definition: hough_3d.h:461
Class implementing a 3D correspondence grouping algorithm that can deal with multiple instances of a ...
Definition: hough_3d.h:146
PointCloud represents the base class in PCL for storing collections of 3D points. ...
SceneCloud::ConstPtr SceneCloudConstPtr
Abstract base class for Correspondence Grouping algorithms.
float local_rf_search_radius_
Search radius for the potential Rf calculation.
Definition: hough_3d.h:470
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: pcl_base.hpp:66
void setInputCloud(const PointCloudConstPtr &cloud) override
Provide a pointer to the input dataset.
Definition: hough_3d.h:184
double hough_threshold_
The minimum number of votes in the Hough space needed to infer the presence of a model instance into ...
Definition: hough_3d.h:455
void setHoughThreshold(double threshold)
Sets the minimum number of votes in the Hough space needed to infer the presence of a model instance ...
Definition: hough_3d.h:276
Eigen::Vector3d min_coord_
Minimum coordinate in the Hough Space.
Definition: hough_3d.h:109
std::vector< Eigen::Vector3f, Eigen::aligned_allocator< Eigen::Vector3f > > model_votes_
The result of the training.
Definition: hough_3d.h:452
ModelRfCloudConstPtr getInputRf() const
Getter for the input dataset&#39;s reference frames.
Definition: hough_3d.h:213
pcl::PointCloud< PointModelRfT > ModelRfCloud
Definition: hough_3d.h:149
std::vector< Eigen::Matrix4f, Eigen::aligned_allocator< Eigen::Matrix4f > > found_transformations_
Transformations found by clusterCorrespondences method.
Definition: hough_3d.h:476
void setUseInterpolation(bool use_interpolation)
Sets whether the vote casting procedure interpolates the score between neighboring bins of the Hough ...
Definition: hough_3d.h:318
double hough_bin_size_
The size of each bin of the hough space.
Definition: hough_3d.h:458
double getHoughBinSize() const
Gets the size of each bin into the Hough space.
Definition: hough_3d.h:307
HoughSpace3D is a 3D voting space.
Definition: hough_3d.h:54
bool getUseInterpolation() const
Gets whether the vote casting procedure interpolates the score between neighboring bins of the Hough ...
Definition: hough_3d.h:330
void setLocalRfSearchRadius(float local_rf_search_radius)
If the Local reference frame has not been set for either the model cloud or the scene cloud...
Definition: hough_3d.h:390
void setSceneRf(const SceneRfCloudConstPtr &scene_rf)
Provide a pointer to the scene dataset&#39;s reference frames.
Definition: hough_3d.h:237
PointCloud::Ptr PointCloudPtr
Definition: hough_3d.h:158
void setHoughBinSize(double bin_size)
Sets the size of each bin into the Hough space.
Definition: hough_3d.h:296