Point Cloud Library (PCL) 1.12.1
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
53namespace 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
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
SampleConsensusModel represents the base model class.
Definition: sac_model.h:70
double radius_min_
The minimum and maximum radius limits for the model.
Definition: sac_model.h:564
shared_ptr< SampleConsensusModel< PointT > > Ptr
Definition: sac_model.h:77
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:588
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: sac_model.h:73
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: sac_model.h:556
PointCloudConstPtr input_
A boost shared pointer to the point cloud data array.
Definition: sac_model.h:553
std::string model_name_
The model name.
Definition: sac_model.h:550
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:591
typename PointCloud::Ptr PointCloudPtr
Definition: sac_model.h:74
shared_ptr< const SampleConsensusModel< PointT > > ConstPtr
Definition: sac_model.h:78
std::vector< double > error_sqr_dists_
A vector holding the distances to the computed model.
Definition: sac_model.h:585
SampleConsensusModelSphere defines a model for 3D sphere segmentation.
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
pcl::SacModel getModelType() const override
Return a unique id for this model (SACMODEL_SPHERE).
SampleConsensusModelSphere(const PointCloudConstPtr &cloud, const Indices &indices, bool random=false)
Constructor for base SampleConsensusModelSphere.
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.
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.
SampleConsensusModelSphere(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelSphere.
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.
bool isModelValid(const Eigen::VectorXf &model_coefficients) const override
Check whether a model is valid given the user constraints.
SampleConsensusModelSphere & operator=(const SampleConsensusModelSphere &source)
Copy constructor.
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.
std::size_t countWithinDistanceStandard(const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i=0) const
This implementation uses no SIMD instructions.
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.
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...
SampleConsensusModelSphere(const SampleConsensusModelSphere &source)
Copy constructor.
~SampleConsensusModelSphere()
Empty destructor.
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.
SacModel
Definition: model_types.h:46
@ SACMODEL_SPHERE
Definition: model_types.h:51
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
Base functor all the models that need non linear optimization must define their own one and implement...
Definition: sac_model.h:679
A point structure representing Euclidean xyz coordinates, and the RGB color.