Point Cloud Library (PCL) 1.15.0
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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#include <pcl/pcl_exports.h>
53
54namespace pcl
55{
56 namespace internal {
57 PCL_EXPORTS int optimizeModelCoefficientsSphere (Eigen::VectorXf& coeff, const Eigen::ArrayXf& pts_x, const Eigen::ArrayXf& pts_y, const Eigen::ArrayXf& pts_z);
58 } // namespace internal
59
60 /** \brief SampleConsensusModelSphere defines a model for 3D sphere segmentation.
61 * The model coefficients are defined as:
62 * - \b center.x : the X coordinate of the sphere's center
63 * - \b center.y : the Y coordinate of the sphere's center
64 * - \b center.z : the Z coordinate of the sphere's center
65 * - \b radius : the sphere's radius
66 *
67 * \author Radu B. Rusu
68 * \ingroup sample_consensus
69 */
70 template <typename PointT>
72 {
73 public:
80
84
85 using Ptr = shared_ptr<SampleConsensusModelSphere<PointT> >;
86 using ConstPtr = shared_ptr<const SampleConsensusModelSphere<PointT>>;
87
88 /** \brief Constructor for base SampleConsensusModelSphere.
89 * \param[in] cloud the input point cloud dataset
90 * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
91 */
93 bool random = false)
94 : SampleConsensusModel<PointT> (cloud, random)
95 {
96 model_name_ = "SampleConsensusModelSphere";
97 sample_size_ = 4;
98 model_size_ = 4;
99 }
100
101 /** \brief Constructor for base SampleConsensusModelSphere.
102 * \param[in] cloud the input point cloud dataset
103 * \param[in] indices a vector of point indices to be used from \a cloud
104 * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
105 */
107 const Indices &indices,
108 bool random = false)
109 : SampleConsensusModel<PointT> (cloud, indices, random)
110 {
111 model_name_ = "SampleConsensusModelSphere";
112 sample_size_ = 4;
113 model_size_ = 4;
114 }
115
116 /** \brief Empty destructor */
117 ~SampleConsensusModelSphere () override = default;
118
119 /** \brief Copy constructor.
120 * \param[in] source the model to copy into this
121 */
124 {
125 *this = source;
126 model_name_ = "SampleConsensusModelSphere";
127 }
128
129 /** \brief Copy constructor.
130 * \param[in] source the model to copy into this
131 */
134 {
136 return (*this);
137 }
138
139 /** \brief Check whether the given index samples can form a valid sphere model, compute the model
140 * coefficients from these samples and store them internally in model_coefficients.
141 * The sphere coefficients are: x, y, z, R.
142 * \param[in] samples the point indices found as possible good candidates for creating a valid model
143 * \param[out] model_coefficients the resultant model coefficients
144 */
145 bool
146 computeModelCoefficients (const Indices &samples,
147 Eigen::VectorXf &model_coefficients) const override;
148
149 /** \brief Compute all distances from the cloud data to a given sphere model.
150 * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
151 * \param[out] distances the resultant estimated distances
152 */
153 void
154 getDistancesToModel (const Eigen::VectorXf &model_coefficients,
155 std::vector<double> &distances) const override;
156
157 /** \brief Select all the points which respect the given model coefficients as inliers.
158 * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
159 * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
160 * \param[out] inliers the resultant model inliers
161 */
162 void
163 selectWithinDistance (const Eigen::VectorXf &model_coefficients,
164 const double threshold,
165 Indices &inliers) override;
166
167 /** \brief Count all the points which respect the given model coefficients as inliers.
168 *
169 * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
170 * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
171 * \return the resultant number of inliers
172 */
173 std::size_t
174 countWithinDistance (const Eigen::VectorXf &model_coefficients,
175 const double threshold) const override;
176
177 /** \brief Recompute the sphere coefficients using the given inlier set and return them to the user.
178 * @note: these are the coefficients of the sphere model after refinement (e.g. after SVD)
179 * \param[in] inliers the data inliers found as supporting the model
180 * \param[in] model_coefficients the initial guess for the optimization
181 * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
182 */
183 void
184 optimizeModelCoefficients (const Indices &inliers,
185 const Eigen::VectorXf &model_coefficients,
186 Eigen::VectorXf &optimized_coefficients) const override;
187
188 /** \brief Create a new point cloud with inliers projected onto the sphere model.
189 * \param[in] inliers the data inliers that we want to project on the sphere model
190 * \param[in] model_coefficients the coefficients of a sphere model
191 * \param[out] projected_points the resultant projected points
192 * \param[in] copy_data_fields set to true if we need to copy the other data fields
193 * \todo implement this.
194 */
195 void
196 projectPoints (const Indices &inliers,
197 const Eigen::VectorXf &model_coefficients,
198 PointCloud &projected_points,
199 bool copy_data_fields = true) const override;
200
201 /** \brief Verify whether a subset of indices verifies the given sphere model coefficients.
202 * \param[in] indices the data indices that need to be tested against the sphere model
203 * \param[in] model_coefficients the sphere model coefficients
204 * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
205 */
206 bool
207 doSamplesVerifyModel (const std::set<index_t> &indices,
208 const Eigen::VectorXf &model_coefficients,
209 const double threshold) const override;
210
211 /** \brief Return a unique id for this model (SACMODEL_SPHERE). */
212 inline pcl::SacModel getModelType () const override { return (SACMODEL_SPHERE); }
213
214 protected:
217
218 /** \brief Check whether a model is valid given the user constraints.
219 * \param[in] model_coefficients the set of model coefficients
220 */
221 bool
222 isModelValid (const Eigen::VectorXf &model_coefficients) const override
223 {
224 if (!SampleConsensusModel<PointT>::isModelValid (model_coefficients))
225 return (false);
226
227 if (radius_min_ != -std::numeric_limits<double>::max() && model_coefficients[3] < radius_min_) {
228 PCL_DEBUG("[SampleConsensusModelSphere::isModelValid] Model radius %g is smaller than user specified minimum radius %g\n", model_coefficients[3], radius_min_);
229 return (false);
230 }
231 if (radius_max_ != std::numeric_limits<double>::max() && model_coefficients[3] > radius_max_) {
232 PCL_DEBUG("[SampleConsensusModelSphere::isModelValid] Model radius %g is bigger than user specified maximum radius %g\n", model_coefficients[3], radius_max_);
233 return (false);
234 }
235
236 return (true);
237 }
238
239 /** \brief Check if a sample of indices results in a good sample of points
240 * indices.
241 * \param[in] samples the resultant index samples
242 */
243 bool
244 isSampleGood(const Indices &samples) const override;
245
246 /** This implementation uses no SIMD instructions. It is not intended for normal use.
247 * See countWithinDistance which automatically uses the fastest implementation.
248 */
249 std::size_t
250 countWithinDistanceStandard (const Eigen::VectorXf &model_coefficients,
251 const double threshold,
252 std::size_t i = 0) const;
253
254#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
255 /** This implementation uses SSE, SSE2, and SSE4.1 instructions. It is not intended for normal use.
256 * See countWithinDistance which automatically uses the fastest implementation.
257 */
258 std::size_t
259 countWithinDistanceSSE (const Eigen::VectorXf &model_coefficients,
260 const double threshold,
261 std::size_t i = 0) const;
262#endif
263
264#if defined (__AVX__) && defined (__AVX2__)
265 /** This implementation uses AVX and AVX2 instructions. It is not intended for normal use.
266 * See countWithinDistance which automatically uses the fastest implementation.
267 */
268 std::size_t
269 countWithinDistanceAVX (const Eigen::VectorXf &model_coefficients,
270 const double threshold,
271 std::size_t i = 0) const;
272#endif
273
274 private:
275#ifdef __AVX__
276 inline __m256 sqr_dist8 (const std::size_t i, const __m256 a_vec, const __m256 b_vec, const __m256 c_vec) const;
277#endif
278
279#ifdef __SSE__
280 inline __m128 sqr_dist4 (const std::size_t i, const __m128 a_vec, const __m128 b_vec, const __m128 c_vec) const;
281#endif
282 };
283}
284
285#ifdef PCL_NO_PRECOMPILE
286#include <pcl/sample_consensus/impl/sac_model_sphere.hpp>
287#endif
PointCloud represents the base class in PCL for storing collections of 3D points.
SampleConsensusModel represents the base model class.
Definition sac_model.h:71
double radius_min_
The minimum and maximum radius limits for the model.
Definition sac_model.h:565
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition sac_model.h:589
typename PointCloud::ConstPtr PointCloudConstPtr
Definition sac_model.h:74
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition sac_model.h:557
PointCloudConstPtr input_
A boost shared pointer to the point cloud data array.
Definition sac_model.h:554
std::string model_name_
The model name.
Definition sac_model.h:551
unsigned int model_size_
The number of coefficients in the model.
Definition sac_model.h:592
typename PointCloud::Ptr PointCloudPtr
Definition sac_model.h:75
std::vector< double > error_sqr_dists_
A vector holding the distances to the computed model.
Definition sac_model.h:586
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.
typename SampleConsensusModel< PointT >::PointCloud PointCloud
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.
typename SampleConsensusModel< PointT >::PointCloudConstPtr PointCloudConstPtr
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.
typename SampleConsensusModel< PointT >::PointCloudPtr PointCloudPtr
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.
shared_ptr< SampleConsensusModelSphere< PointT > > Ptr
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() override=default
Empty destructor.
SampleConsensusModelSphere(const SampleConsensusModelSphere &source)
Copy constructor.
shared_ptr< const SampleConsensusModelSphere< PointT > > ConstPtr
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.
PCL_EXPORTS int optimizeModelCoefficientsSphere(Eigen::VectorXf &coeff, const Eigen::ArrayXf &pts_x, const Eigen::ArrayXf &pts_y, const Eigen::ArrayXf &pts_z)
@ SACMODEL_SPHERE
Definition model_types.h:51
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
A point structure representing Euclidean xyz coordinates, and the RGB color.