Point Cloud Library (PCL) 1.13.0
sac_model_sphere.hpp
1/*
2 * Software License Agreement (BSD License)
3 *
4 * Point Cloud Library (PCL) - www.pointclouds.org
5 * Copyright (c) 2009, Willow Garage, Inc.
6 * Copyright (c) 2012-, Open Perception, Inc.
7 *
8 * All rights reserved.
9 *
10 * Redistribution and use in source and binary forms, with or without
11 * modification, are permitted provided that the following conditions
12 * are met:
13 *
14 * * Redistributions of source code must retain the above copyright
15 * notice, this list of conditions and the following disclaimer.
16 * * Redistributions in binary form must reproduce the above
17 * copyright notice, this list of conditions and the following
18 * disclaimer in the documentation and/or other materials provided
19 * with the distribution.
20 * * Neither the name of the copyright holder(s) nor the names of its
21 * contributors may be used to endorse or promote products derived
22 * from this software without specific prior written permission.
23 *
24 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
25 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
26 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
27 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
28 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
29 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
30 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
31 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
32 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
33 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
34 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
35 * POSSIBILITY OF SUCH DAMAGE.
36 *
37 * $Id$
38 *
39 */
40
41#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
42#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
43
44#include <unsupported/Eigen/NonLinearOptimization> // for LevenbergMarquardt
45#include <pcl/sample_consensus/sac_model_sphere.h>
46
47//////////////////////////////////////////////////////////////////////////
48template <typename PointT> bool
50{
51 if (samples.size () != sample_size_)
52 {
53 PCL_ERROR ("[pcl::SampleConsensusModelSphere::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
54 return (false);
55 }
56 return (true);
57}
58
59//////////////////////////////////////////////////////////////////////////
60template <typename PointT> bool
62 const Indices &samples, Eigen::VectorXf &model_coefficients) const
63{
64 // Need 4 samples
65 if (samples.size () != sample_size_)
66 {
67 PCL_ERROR ("[pcl::SampleConsensusModelSphere::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
68 return (false);
69 }
70
71 // TODO maybe find a more stable algorithm for this?
72 Eigen::Matrix4d temp;
73 for (int i = 0; i < 4; i++)
74 {
75 temp (i, 0) = (*input_)[samples[i]].x;
76 temp (i, 1) = (*input_)[samples[i]].y;
77 temp (i, 2) = (*input_)[samples[i]].z;
78 temp (i, 3) = 1;
79 }
80 const double m11 = temp.determinant ();
81 if (m11 == 0)
82 {
83 return (false); // the points don't define a sphere!
84 }
85
86 for (int i = 0; i < 4; ++i)
87 {
88 temp (i, 0) = ((*input_)[samples[i]].x) * ((*input_)[samples[i]].x) +
89 ((*input_)[samples[i]].y) * ((*input_)[samples[i]].y) +
90 ((*input_)[samples[i]].z) * ((*input_)[samples[i]].z);
91 }
92 const double m12 = temp.determinant ();
93
94 for (int i = 0; i < 4; ++i)
95 {
96 temp (i, 1) = temp (i, 0);
97 temp (i, 0) = (*input_)[samples[i]].x;
98 }
99 const double m13 = temp.determinant ();
100
101 for (int i = 0; i < 4; ++i)
102 {
103 temp (i, 2) = temp (i, 1);
104 temp (i, 1) = (*input_)[samples[i]].y;
105 }
106 const double m14 = temp.determinant ();
107
108 for (int i = 0; i < 4; ++i)
109 {
110 temp (i, 0) = temp (i, 2);
111 temp (i, 1) = (*input_)[samples[i]].x;
112 temp (i, 2) = (*input_)[samples[i]].y;
113 temp (i, 3) = (*input_)[samples[i]].z;
114 }
115 const double m15 = temp.determinant ();
116
117 // Center (x , y, z)
118 model_coefficients.resize (model_size_);
119 model_coefficients[0] = 0.5f * m12 / m11;
120 model_coefficients[1] = 0.5f * m13 / m11;
121 model_coefficients[2] = 0.5f * m14 / m11;
122 // Radius
123 model_coefficients[3] = std::sqrt (model_coefficients[0] * model_coefficients[0] +
124 model_coefficients[1] * model_coefficients[1] +
125 model_coefficients[2] * model_coefficients[2] - m15 / m11);
126
127 PCL_DEBUG ("[pcl::SampleConsensusModelSphere::computeModelCoefficients] Model is (%g,%g,%g,%g)\n",
128 model_coefficients[0], model_coefficients[1], model_coefficients[2], model_coefficients[3]);
129 return (true);
130}
131
132#define AT(POS) ((*input_)[(*indices_)[(POS)]])
133
134#ifdef __AVX__
135// This function computes the squared distances (i.e. the distances without the square root) of 8 points to the center of the sphere
136template <typename PointT> inline __m256 pcl::SampleConsensusModelSphere<PointT>::sqr_dist8 (const std::size_t i, const __m256 a_vec, const __m256 b_vec, const __m256 c_vec) const
137{
138 const __m256 tmp1 = _mm256_sub_ps (_mm256_set_ps (AT(i ).x, AT(i+1).x, AT(i+2).x, AT(i+3).x, AT(i+4).x, AT(i+5).x, AT(i+6).x, AT(i+7).x), a_vec);
139 const __m256 tmp2 = _mm256_sub_ps (_mm256_set_ps (AT(i ).y, AT(i+1).y, AT(i+2).y, AT(i+3).y, AT(i+4).y, AT(i+5).y, AT(i+6).y, AT(i+7).y), b_vec);
140 const __m256 tmp3 = _mm256_sub_ps (_mm256_set_ps (AT(i ).z, AT(i+1).z, AT(i+2).z, AT(i+3).z, AT(i+4).z, AT(i+5).z, AT(i+6).z, AT(i+7).z), c_vec);
141 return _mm256_add_ps (_mm256_add_ps (_mm256_mul_ps (tmp1, tmp1), _mm256_mul_ps (tmp2, tmp2)), _mm256_mul_ps(tmp3, tmp3));
142}
143#endif // ifdef __AVX__
144
145#ifdef __SSE__
146// This function computes the squared distances (i.e. the distances without the square root) of 4 points to the center of the sphere
147template <typename PointT> inline __m128 pcl::SampleConsensusModelSphere<PointT>::sqr_dist4 (const std::size_t i, const __m128 a_vec, const __m128 b_vec, const __m128 c_vec) const
148{
149 const __m128 tmp1 = _mm_sub_ps (_mm_set_ps (AT(i ).x, AT(i+1).x, AT(i+2).x, AT(i+3).x), a_vec);
150 const __m128 tmp2 = _mm_sub_ps (_mm_set_ps (AT(i ).y, AT(i+1).y, AT(i+2).y, AT(i+3).y), b_vec);
151 const __m128 tmp3 = _mm_sub_ps (_mm_set_ps (AT(i ).z, AT(i+1).z, AT(i+2).z, AT(i+3).z), c_vec);
152 return _mm_add_ps (_mm_add_ps (_mm_mul_ps (tmp1, tmp1), _mm_mul_ps (tmp2, tmp2)), _mm_mul_ps(tmp3, tmp3));
153}
154#endif // ifdef __SSE__
155
156#undef AT
157
158//////////////////////////////////////////////////////////////////////////
159template <typename PointT> void
161 const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
162{
163 // Check if the model is valid given the user constraints
164 if (!isModelValid (model_coefficients))
165 {
166 distances.clear ();
167 return;
168 }
169 distances.resize (indices_->size ());
170
171 const Eigen::Vector3f center (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
172 // Iterate through the 3d points and calculate the distances from them to the sphere
173 for (std::size_t i = 0; i < indices_->size (); ++i)
174 {
175 // Calculate the distance from the point to the sphere as the difference between
176 //dist(point,sphere_origin) and sphere_radius
177 distances[i] = std::abs (((*input_)[(*indices_)[i]].getVector3fMap () - center).norm () - model_coefficients[3]);
178 }
180
181//////////////////////////////////////////////////////////////////////////
182template <typename PointT> void
184 const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
185{
186 // Check if the model is valid given the user constraints
187 if (!isModelValid (model_coefficients))
188 {
189 inliers.clear ();
190 return;
192
193 inliers.clear ();
194 error_sqr_dists_.clear ();
195 inliers.reserve (indices_->size ());
196 error_sqr_dists_.reserve (indices_->size ());
197
198 const float sqr_inner_radius = (model_coefficients[3] <= threshold ? 0.0f : (model_coefficients[3] - threshold) * (model_coefficients[3] - threshold));
199 const float sqr_outer_radius = (model_coefficients[3] + threshold) * (model_coefficients[3] + threshold);
200 const Eigen::Vector3f center (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
201 // Iterate through the 3d points and calculate the distances from them to the sphere
202 for (std::size_t i = 0; i < indices_->size (); ++i)
203 {
204 // To avoid sqrt computation: consider one larger sphere (radius + threshold) and one smaller sphere (radius - threshold).
205 // Valid if point is in larger sphere, but not in smaller sphere.
206 const float sqr_dist = ((*input_)[(*indices_)[i]].getVector3fMap () - center).squaredNorm ();
207 if ((sqr_dist <= sqr_outer_radius) && (sqr_dist >= sqr_inner_radius))
208 {
209 // Returns the indices of the points whose distances are smaller than the threshold
210 inliers.push_back ((*indices_)[i]);
211 // Only compute exact distance if necessary (if point is inlier)
212 error_sqr_dists_.push_back (static_cast<double> (std::abs (std::sqrt (sqr_dist) - model_coefficients[3])));
213 }
214 }
215}
216
217//////////////////////////////////////////////////////////////////////////
218template <typename PointT> std::size_t
220 const Eigen::VectorXf &model_coefficients, const double threshold) const
221{
222 // Check if the model is valid given the user constraints
223 if (!isModelValid (model_coefficients))
224 return (0);
225
226#if defined (__AVX__) && defined (__AVX2__)
227 return countWithinDistanceAVX (model_coefficients, threshold);
228#elif defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
229 return countWithinDistanceSSE (model_coefficients, threshold);
230#else
231 return countWithinDistanceStandard (model_coefficients, threshold);
232#endif
233}
234
235//////////////////////////////////////////////////////////////////////////
236template <typename PointT> std::size_t
238 const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
239{
240 std::size_t nr_p = 0;
241 const float sqr_inner_radius = (model_coefficients[3] <= threshold ? 0.0f : (model_coefficients[3] - threshold) * (model_coefficients[3] - threshold));
242 const float sqr_outer_radius = (model_coefficients[3] + threshold) * (model_coefficients[3] + threshold);
243 const Eigen::Vector3f center (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
244 // Iterate through the 3d points and calculate the distances from them to the sphere
245 for (; i < indices_->size (); ++i)
246 {
247 // To avoid sqrt computation: consider one larger sphere (radius + threshold) and one smaller sphere (radius - threshold).
248 // Valid if point is in larger sphere, but not in smaller sphere.
249 const float sqr_dist = ((*input_)[(*indices_)[i]].getVector3fMap () - center).squaredNorm ();
250 if ((sqr_dist <= sqr_outer_radius) && (sqr_dist >= sqr_inner_radius))
251 nr_p++;
252 }
253 return (nr_p);
254}
255
256//////////////////////////////////////////////////////////////////////////
257#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
258template <typename PointT> std::size_t
260 const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
261{
262 std::size_t nr_p = 0;
263 const __m128 a_vec = _mm_set1_ps (model_coefficients[0]);
264 const __m128 b_vec = _mm_set1_ps (model_coefficients[1]);
265 const __m128 c_vec = _mm_set1_ps (model_coefficients[2]);
266 // To avoid sqrt computation: consider one larger sphere (radius + threshold) and one smaller sphere (radius - threshold). Valid if point is in larger sphere, but not in smaller sphere.
267 const __m128 sqr_inner_radius = _mm_set1_ps ((model_coefficients[3] <= threshold ? 0.0 : (model_coefficients[3]-threshold)*(model_coefficients[3]-threshold)));
268 const __m128 sqr_outer_radius = _mm_set1_ps ((model_coefficients[3]+threshold)*(model_coefficients[3]+threshold));
269 __m128i res = _mm_set1_epi32(0); // This corresponds to nr_p: 4 32bit integers that, summed together, hold the number of inliers
270 for (; (i + 4) <= indices_->size (); i += 4)
271 {
272 const __m128 sqr_dist = sqr_dist4 (i, a_vec, b_vec, c_vec);
273 const __m128 mask = _mm_and_ps (_mm_cmplt_ps (sqr_inner_radius, sqr_dist), _mm_cmplt_ps (sqr_dist, sqr_outer_radius)); // The mask contains 1 bits if the corresponding points are inliers, else 0 bits
274 res = _mm_add_epi32 (res, _mm_and_si128 (_mm_set1_epi32 (1), _mm_castps_si128 (mask))); // The latter part creates a vector with ones (as 32bit integers) where the points are inliers
275 //const int res = _mm_movemask_ps (mask);
276 //if (res & 1) nr_p++;
277 //if (res & 2) nr_p++;
278 //if (res & 4) nr_p++;
279 //if (res & 8) nr_p++;
280 }
281 nr_p += _mm_extract_epi32 (res, 0);
282 nr_p += _mm_extract_epi32 (res, 1);
283 nr_p += _mm_extract_epi32 (res, 2);
284 nr_p += _mm_extract_epi32 (res, 3);
285
286 // Process the remaining points (at most 3)
287 nr_p += countWithinDistanceStandard (model_coefficients, threshold, i);
288 return (nr_p);
289}
290#endif
291
292//////////////////////////////////////////////////////////////////////////
293#if defined (__AVX__) && defined (__AVX2__)
294template <typename PointT> std::size_t
296 const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
297{
298 std::size_t nr_p = 0;
299 const __m256 a_vec = _mm256_set1_ps (model_coefficients[0]);
300 const __m256 b_vec = _mm256_set1_ps (model_coefficients[1]);
301 const __m256 c_vec = _mm256_set1_ps (model_coefficients[2]);
302 // To avoid sqrt computation: consider one larger sphere (radius + threshold) and one smaller sphere (radius - threshold). Valid if point is in larger sphere, but not in smaller sphere.
303 const __m256 sqr_inner_radius = _mm256_set1_ps ((model_coefficients[3] <= threshold ? 0.0 : (model_coefficients[3]-threshold)*(model_coefficients[3]-threshold)));
304 const __m256 sqr_outer_radius = _mm256_set1_ps ((model_coefficients[3]+threshold)*(model_coefficients[3]+threshold));
305 __m256i res = _mm256_set1_epi32(0); // This corresponds to nr_p: 8 32bit integers that, summed together, hold the number of inliers
306 for (; (i + 8) <= indices_->size (); i += 8)
307 {
308 const __m256 sqr_dist = sqr_dist8 (i, a_vec, b_vec, c_vec);
309 const __m256 mask = _mm256_and_ps (_mm256_cmp_ps (sqr_inner_radius, sqr_dist, _CMP_LT_OQ), _mm256_cmp_ps (sqr_dist, sqr_outer_radius, _CMP_LT_OQ)); // The mask contains 1 bits if the corresponding points are inliers, else 0 bits
310 res = _mm256_add_epi32 (res, _mm256_and_si256 (_mm256_set1_epi32 (1), _mm256_castps_si256 (mask))); // The latter part creates a vector with ones (as 32bit integers) where the points are inliers
311 //const int res = _mm256_movemask_ps (mask);
312 //if (res & 1) nr_p++;
313 //if (res & 2) nr_p++;
314 //if (res & 4) nr_p++;
315 //if (res & 8) nr_p++;
316 //if (res & 16) nr_p++;
317 //if (res & 32) nr_p++;
318 //if (res & 64) nr_p++;
319 //if (res & 128) nr_p++;
320 }
321 nr_p += _mm256_extract_epi32 (res, 0);
322 nr_p += _mm256_extract_epi32 (res, 1);
323 nr_p += _mm256_extract_epi32 (res, 2);
324 nr_p += _mm256_extract_epi32 (res, 3);
325 nr_p += _mm256_extract_epi32 (res, 4);
326 nr_p += _mm256_extract_epi32 (res, 5);
327 nr_p += _mm256_extract_epi32 (res, 6);
328 nr_p += _mm256_extract_epi32 (res, 7);
329
330 // Process the remaining points (at most 7)
331 nr_p += countWithinDistanceStandard (model_coefficients, threshold, i);
332 return (nr_p);
333}
334#endif
335
336//////////////////////////////////////////////////////////////////////////
337template <typename PointT> void
339 const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
340{
341 optimized_coefficients = model_coefficients;
342
343 // Needs a set of valid model coefficients
344 if (!isModelValid (model_coefficients))
345 {
346 PCL_ERROR ("[pcl::SampleConsensusModelSphere::optimizeModelCoefficients] Given model is invalid!\n");
347 return;
348 }
349
350 // Need more than the minimum sample size to make a difference
351 if (inliers.size () <= sample_size_)
352 {
353 PCL_ERROR ("[pcl::SampleConsensusModelSphere::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
354 return;
355 }
356
357 OptimizationFunctor functor (this, inliers);
358 Eigen::NumericalDiff<OptimizationFunctor> num_diff (functor);
359 Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, float> lm (num_diff);
360 int info = lm.minimize (optimized_coefficients);
361
362 // Compute the L2 norm of the residuals
363 PCL_DEBUG ("[pcl::SampleConsensusModelSphere::optimizeModelCoefficients] LM solver finished with exit code %i, having a residual norm of %g. \nInitial solution: %g %g %g %g \nFinal solution: %g %g %g %g\n",
364 info, lm.fvec.norm (), model_coefficients[0], model_coefficients[1], model_coefficients[2], model_coefficients[3], optimized_coefficients[0], optimized_coefficients[1], optimized_coefficients[2], optimized_coefficients[3]);
365}
366
367//////////////////////////////////////////////////////////////////////////
368template <typename PointT> void
370 const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
371{
372 // Needs a valid set of model coefficients
373 if (!isModelValid (model_coefficients))
374 {
375 PCL_ERROR ("[pcl::SampleConsensusModelSphere::projectPoints] Given model is invalid!\n");
376 return;
377 }
378
379 projected_points.header = input_->header;
380 projected_points.is_dense = input_->is_dense;
381
382 // C : sphere center
383 const Eigen::Vector3d C (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
384 // r : radius
385 const double r = model_coefficients[3];
386
387 // Copy all the data fields from the input cloud to the projected one?
388 if (copy_data_fields)
389 {
390 // Allocate enough space and copy the basics
391 projected_points.resize (input_->size ());
392 projected_points.width = input_->width;
393 projected_points.height = input_->height;
394
395 using FieldList = typename pcl::traits::fieldList<PointT>::type;
396 // Iterate over each point
397 for (std::size_t i = 0; i < projected_points.points.size (); ++i)
398 // Iterate over each dimension
399 pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> (input_->points[i], projected_points.points[i]));
400
401 // Iterate through the 3d points and calculate the distances from them to the sphere
402 for (std::size_t i = 0; i < inliers.size (); ++i)
403 {
404 // what i have:
405 // P : Sample Point
406 const Eigen::Vector3d P (input_->points[inliers[i]].x, input_->points[inliers[i]].y, input_->points[inliers[i]].z);
407
408 const Eigen::Vector3d direction = (P - C).normalized();
409
410 // K : Point on Sphere
411 const Eigen::Vector3d K = C + r * direction;
412
413 projected_points.points[inliers[i]].x = static_cast<float> (K[0]);
414 projected_points.points[inliers[i]].y = static_cast<float> (K[1]);
415 projected_points.points[inliers[i]].z = static_cast<float> (K[2]);
416 }
417 }
418 else
419 {
420 // Allocate enough space and copy the basics
421 projected_points.resize (inliers.size ());
422 projected_points.width = static_cast<uint32_t> (inliers.size ());
423 projected_points.height = 1;
424
425 using FieldList = typename pcl::traits::fieldList<PointT>::type;
426 // Iterate over each point
427 for (std::size_t i = 0; i < inliers.size (); ++i)
428 // Iterate over each dimension
429 pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> (input_->points[inliers[i]], projected_points.points[i]));
430
431 // Iterate through the 3d points and calculate the distances from them to the plane
432 for (std::size_t i = 0; i < inliers.size (); ++i)
433 {
434 // what i have:
435 // P : Sample Point
436 const Eigen::Vector3d P (input_->points[inliers[i]].x, input_->points[inliers[i]].y, input_->points[inliers[i]].z);
437
438 const Eigen::Vector3d direction = (P - C).normalized();
439
440 // K : Point on Sphere
441 const Eigen::Vector3d K = C + r * direction;
442
443 projected_points.points[i].x = static_cast<float> (K[0]);
444 projected_points.points[i].y = static_cast<float> (K[1]);
445 projected_points.points[i].z = static_cast<float> (K[2]);
446 }
447 }
448}
449
450//////////////////////////////////////////////////////////////////////////
451template <typename PointT> bool
453 const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
454{
455 // Needs a valid model coefficients
456 if (!isModelValid (model_coefficients))
457 {
458 PCL_ERROR ("[pcl::SampleConsensusModelSphere::doSamplesVerifyModel] Given model is invalid!\n");
459 return (false);
460 }
461
462 const float sqr_inner_radius = (model_coefficients[3] <= threshold ? 0.0f : (model_coefficients[3] - threshold) * (model_coefficients[3] - threshold));
463 const float sqr_outer_radius = (model_coefficients[3] + threshold) * (model_coefficients[3] + threshold);
464 const Eigen::Vector3f center (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
465 for (const auto &index : indices)
466 {
467 // To avoid sqrt computation: consider one larger sphere (radius + threshold) and one smaller sphere (radius - threshold).
468 // Valid if point is in larger sphere, but not in smaller sphere.
469 const float sqr_dist = ((*input_)[index].getVector3fMap () - center).squaredNorm ();
470 if ((sqr_dist > sqr_outer_radius) || (sqr_dist < sqr_inner_radius))
471 {
472 return (false);
473 }
474 }
475
476 return (true);
477}
478
479#define PCL_INSTANTIATE_SampleConsensusModelSphere(T) template class PCL_EXPORTS pcl::SampleConsensusModelSphere<T>;
480
481#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
482
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:403
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:462
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:398
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:392
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:400
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:395
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.
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.
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 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...
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.
@ K
Definition: norms.h:54
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
Helper functor structure for concatenate.
Definition: concatenate.h:50