Point Cloud Library (PCL)  1.11.1-dev
sac_model_sphere.h
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40 
41 #pragma once
42 
43 #ifdef __SSE__
44 #include <xmmintrin.h> // for __m128
45 #endif // ifdef __SSE__
46 #ifdef __AVX__
47 #include <immintrin.h> // for __m256
48 #endif // ifdef __AVX__
49 
50 #include <pcl/sample_consensus/sac_model.h>
51 #include <pcl/sample_consensus/model_types.h>
52 
53 namespace pcl
54 {
55  /** \brief SampleConsensusModelSphere defines a model for 3D sphere segmentation.
56  * The model coefficients are defined as:
57  * - \b center.x : the X coordinate of the sphere's center
58  * - \b center.y : the Y coordinate of the sphere's center
59  * - \b center.z : the Z coordinate of the sphere's center
60  * - \b radius : the sphere's radius
61  *
62  * \author Radu B. Rusu
63  * \ingroup sample_consensus
64  */
65  template <typename PointT>
67  {
68  public:
75 
79 
80  using Ptr = shared_ptr<SampleConsensusModelSphere<PointT> >;
81  using ConstPtr = shared_ptr<const SampleConsensusModelSphere<PointT>>;
82 
83  /** \brief Constructor for base SampleConsensusModelSphere.
84  * \param[in] cloud the input point cloud dataset
85  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
86  */
88  bool random = false)
89  : SampleConsensusModel<PointT> (cloud, random)
90  {
91  model_name_ = "SampleConsensusModelSphere";
92  sample_size_ = 4;
93  model_size_ = 4;
94  }
95 
96  /** \brief Constructor for base SampleConsensusModelSphere.
97  * \param[in] cloud the input point cloud dataset
98  * \param[in] indices a vector of point indices to be used from \a cloud
99  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
100  */
102  const Indices &indices,
103  bool random = false)
104  : SampleConsensusModel<PointT> (cloud, indices, random)
105  {
106  model_name_ = "SampleConsensusModelSphere";
107  sample_size_ = 4;
108  model_size_ = 4;
109  }
110 
111  /** \brief Empty destructor */
113 
114  /** \brief Copy constructor.
115  * \param[in] source the model to copy into this
116  */
119  {
120  *this = source;
121  model_name_ = "SampleConsensusModelSphere";
122  }
123 
124  /** \brief Copy constructor.
125  * \param[in] source the model to copy into this
126  */
129  {
131  return (*this);
132  }
133 
134  /** \brief Check whether the given index samples can form a valid sphere model, compute the model
135  * coefficients from these samples and store them internally in model_coefficients.
136  * The sphere coefficients are: x, y, z, R.
137  * \param[in] samples the point indices found as possible good candidates for creating a valid model
138  * \param[out] model_coefficients the resultant model coefficients
139  */
140  bool
141  computeModelCoefficients (const Indices &samples,
142  Eigen::VectorXf &model_coefficients) const override;
143 
144  /** \brief Compute all distances from the cloud data to a given sphere model.
145  * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
146  * \param[out] distances the resultant estimated distances
147  */
148  void
149  getDistancesToModel (const Eigen::VectorXf &model_coefficients,
150  std::vector<double> &distances) const override;
151 
152  /** \brief Select all the points which respect the given model coefficients as inliers.
153  * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
154  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
155  * \param[out] inliers the resultant model inliers
156  */
157  void
158  selectWithinDistance (const Eigen::VectorXf &model_coefficients,
159  const double threshold,
160  Indices &inliers) override;
161 
162  /** \brief Count all the points which respect the given model coefficients as inliers.
163  *
164  * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
165  * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
166  * \return the resultant number of inliers
167  */
168  std::size_t
169  countWithinDistance (const Eigen::VectorXf &model_coefficients,
170  const double threshold) const override;
171 
172  /** \brief Recompute the sphere coefficients using the given inlier set and return them to the user.
173  * @note: these are the coefficients of the sphere model after refinement (e.g. after SVD)
174  * \param[in] inliers the data inliers found as supporting the model
175  * \param[in] model_coefficients the initial guess for the optimization
176  * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
177  */
178  void
179  optimizeModelCoefficients (const Indices &inliers,
180  const Eigen::VectorXf &model_coefficients,
181  Eigen::VectorXf &optimized_coefficients) const override;
182 
183  /** \brief Create a new point cloud with inliers projected onto the sphere model.
184  * \param[in] inliers the data inliers that we want to project on the sphere model
185  * \param[in] model_coefficients the coefficients of a sphere model
186  * \param[out] projected_points the resultant projected points
187  * \param[in] copy_data_fields set to true if we need to copy the other data fields
188  * \todo implement this.
189  */
190  void
191  projectPoints (const Indices &inliers,
192  const Eigen::VectorXf &model_coefficients,
193  PointCloud &projected_points,
194  bool copy_data_fields = true) const override;
195 
196  /** \brief Verify whether a subset of indices verifies the given sphere model coefficients.
197  * \param[in] indices the data indices that need to be tested against the sphere model
198  * \param[in] model_coefficients the sphere model coefficients
199  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
200  */
201  bool
202  doSamplesVerifyModel (const std::set<index_t> &indices,
203  const Eigen::VectorXf &model_coefficients,
204  const double threshold) const override;
205 
206  /** \brief Return a unique id for this model (SACMODEL_SPHERE). */
207  inline pcl::SacModel getModelType () const override { return (SACMODEL_SPHERE); }
208 
209  protected:
212 
213  /** \brief Check whether a model is valid given the user constraints.
214  * \param[in] model_coefficients the set of model coefficients
215  */
216  bool
217  isModelValid (const Eigen::VectorXf &model_coefficients) const override
218  {
219  if (!SampleConsensusModel<PointT>::isModelValid (model_coefficients))
220  return (false);
221 
222  if (radius_min_ != -std::numeric_limits<double>::max() && model_coefficients[3] < radius_min_)
223  return (false);
224  if (radius_max_ != std::numeric_limits<double>::max() && model_coefficients[3] > radius_max_)
225  return (false);
226 
227  return (true);
228  }
229 
230  /** \brief Check if a sample of indices results in a good sample of points
231  * indices.
232  * \param[in] samples the resultant index samples
233  */
234  bool
235  isSampleGood(const Indices &samples) const override;
236 
237  /** This implementation uses no SIMD instructions. It is not intended for normal use.
238  * See countWithinDistance which automatically uses the fastest implementation.
239  */
240  std::size_t
241  countWithinDistanceStandard (const Eigen::VectorXf &model_coefficients,
242  const double threshold,
243  std::size_t i = 0) const;
244 
245 #if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
246  /** This implementation uses SSE, SSE2, and SSE4.1 instructions. It is not intended for normal use.
247  * See countWithinDistance which automatically uses the fastest implementation.
248  */
249  std::size_t
250  countWithinDistanceSSE (const Eigen::VectorXf &model_coefficients,
251  const double threshold,
252  std::size_t i = 0) const;
253 #endif
254 
255 #if defined (__AVX__) && defined (__AVX2__)
256  /** This implementation uses AVX and AVX2 instructions. It is not intended for normal use.
257  * See countWithinDistance which automatically uses the fastest implementation.
258  */
259  std::size_t
260  countWithinDistanceAVX (const Eigen::VectorXf &model_coefficients,
261  const double threshold,
262  std::size_t i = 0) const;
263 #endif
264 
265  private:
266  struct OptimizationFunctor : pcl::Functor<float>
267  {
268  /** Functor constructor
269  * \param[in] indices the indices of data points to evaluate
270  * \param[in] estimator pointer to the estimator object
271  */
272  OptimizationFunctor (const pcl::SampleConsensusModelSphere<PointT> *model, const Indices& indices) :
273  pcl::Functor<float> (indices.size ()), model_ (model), indices_ (indices) {}
274 
275  /** Cost function to be minimized
276  * \param[in] x the variables array
277  * \param[out] fvec the resultant functions evaluations
278  * \return 0
279  */
280  int
281  operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
282  {
283  Eigen::Vector4f cen_t;
284  cen_t[3] = 0;
285  for (int i = 0; i < values (); ++i)
286  {
287  // Compute the difference between the center of the sphere and the datapoint X_i
288  cen_t.head<3>() = (*model_->input_)[indices_[i]].getVector3fMap() - x.head<3>();
289 
290  // g = sqrt ((x-a)^2 + (y-b)^2 + (z-c)^2) - R
291  fvec[i] = std::sqrt (cen_t.dot (cen_t)) - x[3];
292  }
293  return (0);
294  }
295 
297  const Indices &indices_;
298  };
299 
300 #ifdef __AVX__
301  inline __m256 sqr_dist8 (const std::size_t i, const __m256 a_vec, const __m256 b_vec, const __m256 c_vec) const;
302 #endif
303 
304 #ifdef __SSE__
305  inline __m128 sqr_dist4 (const std::size_t i, const __m128 a_vec, const __m128 b_vec, const __m128 c_vec) const;
306 #endif
307  };
308 }
309 
310 #ifdef PCL_NO_PRECOMPILE
311 #include <pcl/sample_consensus/impl/sac_model_sphere.hpp>
312 #endif
pcl
Definition: convolution.h:46
pcl::SampleConsensusModelSphere::countWithinDistance
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
Definition: sac_model_sphere.hpp:216
pcl::SampleConsensusModelSphere::isSampleGood
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_sphere.hpp:49
pcl::SampleConsensusModelSphere::~SampleConsensusModelSphere
~SampleConsensusModelSphere()
Empty destructor.
Definition: sac_model_sphere.h:112
pcl::SampleConsensusModel::sample_size_
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:588
pcl::SampleConsensusModelSphere::SampleConsensusModelSphere
SampleConsensusModelSphere(const PointCloudConstPtr &cloud, const Indices &indices, bool random=false)
Constructor for base SampleConsensusModelSphere.
Definition: sac_model_sphere.h:101
pcl::SampleConsensusModel::model_size_
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:591
pcl::PointCloud< pcl::PointXYZRGB >
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:628
pcl::SampleConsensusModelSphere::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all distances from the cloud data to a given sphere model.
Definition: sac_model_sphere.hpp:157
pcl::SampleConsensusModelSphere::optimizeModelCoefficients
void optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the sphere coefficients using the given inlier set and return them to the user.
Definition: sac_model_sphere.hpp:335
pcl::Functor
Base functor all the models that need non linear optimization must define their own one and implement...
Definition: sac_model.h:678
pcl::SampleConsensusModelSphere::countWithinDistanceStandard
std::size_t countWithinDistanceStandard(const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i=0) const
This implementation uses no SIMD instructions.
Definition: sac_model_sphere.hpp:234
pcl::SacModel
SacModel
Definition: model_types.h:45
pcl::SampleConsensusModelSphere::computeModelCoefficients
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid sphere model, compute the model coefficients f...
Definition: sac_model_sphere.hpp:61
pcl::SampleConsensusModel::indices_
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: sac_model.h:556
pcl::SampleConsensusModel< pcl::PointXYZRGB >::ConstPtr
shared_ptr< const SampleConsensusModel< pcl::PointXYZRGB > > ConstPtr
Definition: sac_model.h:78
pcl::SampleConsensusModelSphere::getModelType
pcl::SacModel getModelType() const override
Return a unique id for this model (SACMODEL_SPHERE).
Definition: sac_model_sphere.h:207
pcl::SACMODEL_SPHERE
@ SACMODEL_SPHERE
Definition: model_types.h:51
pcl::SampleConsensusModel::radius_min_
double radius_min_
The minimum and maximum radius limits for the model.
Definition: sac_model.h:564
pcl::SampleConsensusModel< pcl::PointXYZRGB >::Ptr
shared_ptr< SampleConsensusModel< pcl::PointXYZRGB > > Ptr
Definition: sac_model.h:77
pcl::SampleConsensusModel::model_name_
std::string model_name_
The model name.
Definition: sac_model.h:550
pcl::SampleConsensusModel::radius_max_
double radius_max_
Definition: sac_model.h:564
pcl::SampleConsensusModelSphere::SampleConsensusModelSphere
SampleConsensusModelSphere(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelSphere.
Definition: sac_model_sphere.h:87
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:131
pcl::SampleConsensusModelSphere::doSamplesVerifyModel
bool doSamplesVerifyModel(const std::set< index_t > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given sphere model coefficients.
Definition: sac_model_sphere.hpp:389
pcl::SampleConsensusModel< pcl::PointXYZRGB >::PointCloudConstPtr
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: sac_model.h:73
pcl::SampleConsensusModelSphere::isModelValid
bool isModelValid(const Eigen::VectorXf &model_coefficients) const override
Check whether a model is valid given the user constraints.
Definition: sac_model_sphere.h:217
pcl::SampleConsensusModelSphere::SampleConsensusModelSphere
SampleConsensusModelSphere(const SampleConsensusModelSphere &source)
Copy constructor.
Definition: sac_model_sphere.h:117
pcl::SampleConsensusModelSphere::projectPoints
void projectPoints(const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override
Create a new point cloud with inliers projected onto the sphere model.
Definition: sac_model_sphere.hpp:366
pcl::SampleConsensusModelSphere::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
Definition: sac_model_sphere.hpp:180
pcl::SampleConsensusModelSphere::operator=
SampleConsensusModelSphere & operator=(const SampleConsensusModelSphere &source)
Copy constructor.
Definition: sac_model_sphere.h:128
pcl::SampleConsensusModel< pcl::PointXYZRGB >::PointCloudPtr
typename PointCloud::Ptr PointCloudPtr
Definition: sac_model.h:74
pcl::SampleConsensusModel
SampleConsensusModel represents the base model class.
Definition: sac_model.h:69
pcl::SampleConsensusModelSphere
SampleConsensusModelSphere defines a model for 3D sphere segmentation.
Definition: sac_model_sphere.h:66