Point Cloud Library (PCL) 1.14.0
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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
53namespace pcl
54{
55 namespace internal {
56 int optimizeModelCoefficientsSphere (Eigen::VectorXf& coeff, const Eigen::ArrayXf& pts_x, const Eigen::ArrayXf& pts_y, const Eigen::ArrayXf& pts_z);
57 } // namespace internal
58
59 /** \brief SampleConsensusModelSphere defines a model for 3D sphere segmentation.
60 * The model coefficients are defined as:
61 * - \b center.x : the X coordinate of the sphere's center
62 * - \b center.y : the Y coordinate of the sphere's center
63 * - \b center.z : the Z coordinate of the sphere's center
64 * - \b radius : the sphere's radius
65 *
66 * \author Radu B. Rusu
67 * \ingroup sample_consensus
68 */
69 template <typename PointT>
71 {
72 public:
79
83
84 using Ptr = shared_ptr<SampleConsensusModelSphere<PointT> >;
85 using ConstPtr = shared_ptr<const SampleConsensusModelSphere<PointT>>;
86
87 /** \brief Constructor for base SampleConsensusModelSphere.
88 * \param[in] cloud the input point cloud dataset
89 * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
90 */
92 bool random = false)
93 : SampleConsensusModel<PointT> (cloud, random)
94 {
95 model_name_ = "SampleConsensusModelSphere";
96 sample_size_ = 4;
97 model_size_ = 4;
98 }
99
100 /** \brief Constructor for base SampleConsensusModelSphere.
101 * \param[in] cloud the input point cloud dataset
102 * \param[in] indices a vector of point indices to be used from \a cloud
103 * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
104 */
106 const Indices &indices,
107 bool random = false)
108 : SampleConsensusModel<PointT> (cloud, indices, random)
109 {
110 model_name_ = "SampleConsensusModelSphere";
111 sample_size_ = 4;
112 model_size_ = 4;
113 }
114
115 /** \brief Empty destructor */
116 ~SampleConsensusModelSphere () override = default;
117
118 /** \brief Copy constructor.
119 * \param[in] source the model to copy into this
120 */
123 {
124 *this = source;
125 model_name_ = "SampleConsensusModelSphere";
126 }
127
128 /** \brief Copy constructor.
129 * \param[in] source the model to copy into this
130 */
133 {
135 return (*this);
136 }
137
138 /** \brief Check whether the given index samples can form a valid sphere model, compute the model
139 * coefficients from these samples and store them internally in model_coefficients.
140 * The sphere coefficients are: x, y, z, R.
141 * \param[in] samples the point indices found as possible good candidates for creating a valid model
142 * \param[out] model_coefficients the resultant model coefficients
143 */
144 bool
145 computeModelCoefficients (const Indices &samples,
146 Eigen::VectorXf &model_coefficients) const override;
147
148 /** \brief Compute all distances from the cloud data to a given sphere model.
149 * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
150 * \param[out] distances the resultant estimated distances
151 */
152 void
153 getDistancesToModel (const Eigen::VectorXf &model_coefficients,
154 std::vector<double> &distances) const override;
155
156 /** \brief Select all the points which respect the given model coefficients as inliers.
157 * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
158 * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
159 * \param[out] inliers the resultant model inliers
160 */
161 void
162 selectWithinDistance (const Eigen::VectorXf &model_coefficients,
163 const double threshold,
164 Indices &inliers) override;
165
166 /** \brief Count all the points which respect the given model coefficients as inliers.
167 *
168 * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
169 * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
170 * \return the resultant number of inliers
171 */
172 std::size_t
173 countWithinDistance (const Eigen::VectorXf &model_coefficients,
174 const double threshold) const override;
175
176 /** \brief Recompute the sphere coefficients using the given inlier set and return them to the user.
177 * @note: these are the coefficients of the sphere model after refinement (e.g. after SVD)
178 * \param[in] inliers the data inliers found as supporting the model
179 * \param[in] model_coefficients the initial guess for the optimization
180 * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
181 */
182 void
183 optimizeModelCoefficients (const Indices &inliers,
184 const Eigen::VectorXf &model_coefficients,
185 Eigen::VectorXf &optimized_coefficients) const override;
186
187 /** \brief Create a new point cloud with inliers projected onto the sphere model.
188 * \param[in] inliers the data inliers that we want to project on the sphere model
189 * \param[in] model_coefficients the coefficients of a sphere model
190 * \param[out] projected_points the resultant projected points
191 * \param[in] copy_data_fields set to true if we need to copy the other data fields
192 * \todo implement this.
193 */
194 void
195 projectPoints (const Indices &inliers,
196 const Eigen::VectorXf &model_coefficients,
197 PointCloud &projected_points,
198 bool copy_data_fields = true) const override;
199
200 /** \brief Verify whether a subset of indices verifies the given sphere model coefficients.
201 * \param[in] indices the data indices that need to be tested against the sphere model
202 * \param[in] model_coefficients the sphere model coefficients
203 * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
204 */
205 bool
206 doSamplesVerifyModel (const std::set<index_t> &indices,
207 const Eigen::VectorXf &model_coefficients,
208 const double threshold) const override;
209
210 /** \brief Return a unique id for this model (SACMODEL_SPHERE). */
211 inline pcl::SacModel getModelType () const override { return (SACMODEL_SPHERE); }
212
213 protected:
216
217 /** \brief Check whether a model is valid given the user constraints.
218 * \param[in] model_coefficients the set of model coefficients
219 */
220 bool
221 isModelValid (const Eigen::VectorXf &model_coefficients) const override
222 {
223 if (!SampleConsensusModel<PointT>::isModelValid (model_coefficients))
224 return (false);
225
226 if (radius_min_ != -std::numeric_limits<double>::max() && model_coefficients[3] < radius_min_) {
227 PCL_DEBUG("[SampleConsensusModelSphere::isModelValid] Model radius %g is smaller than user specified minimum radius %g\n", model_coefficients[3], radius_min_);
228 return (false);
229 }
230 if (radius_max_ != std::numeric_limits<double>::max() && model_coefficients[3] > radius_max_) {
231 PCL_DEBUG("[SampleConsensusModelSphere::isModelValid] Model radius %g is bigger than user specified maximum radius %g\n", model_coefficients[3], radius_max_);
232 return (false);
233 }
234
235 return (true);
236 }
237
238 /** \brief Check if a sample of indices results in a good sample of points
239 * indices.
240 * \param[in] samples the resultant index samples
241 */
242 bool
243 isSampleGood(const Indices &samples) const override;
244
245 /** This implementation uses no SIMD instructions. It is not intended for normal use.
246 * See countWithinDistance which automatically uses the fastest implementation.
247 */
248 std::size_t
249 countWithinDistanceStandard (const Eigen::VectorXf &model_coefficients,
250 const double threshold,
251 std::size_t i = 0) const;
252
253#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
254 /** This implementation uses SSE, SSE2, and SSE4.1 instructions. It is not intended for normal use.
255 * See countWithinDistance which automatically uses the fastest implementation.
256 */
257 std::size_t
258 countWithinDistanceSSE (const Eigen::VectorXf &model_coefficients,
259 const double threshold,
260 std::size_t i = 0) const;
261#endif
262
263#if defined (__AVX__) && defined (__AVX2__)
264 /** This implementation uses AVX and AVX2 instructions. It is not intended for normal use.
265 * See countWithinDistance which automatically uses the fastest implementation.
266 */
267 std::size_t
268 countWithinDistanceAVX (const Eigen::VectorXf &model_coefficients,
269 const double threshold,
270 std::size_t i = 0) const;
271#endif
272
273 private:
274#ifdef __AVX__
275 inline __m256 sqr_dist8 (const std::size_t i, const __m256 a_vec, const __m256 b_vec, const __m256 c_vec) const;
276#endif
277
278#ifdef __SSE__
279 inline __m128 sqr_dist4 (const std::size_t i, const __m128 a_vec, const __m128 b_vec, const __m128 c_vec) const;
280#endif
281 };
282}
283
284#ifdef PCL_NO_PRECOMPILE
285#include <pcl/sample_consensus/impl/sac_model_sphere.hpp>
286#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.
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.