Point Cloud Library (PCL) 1.12.0
iss_3d.hpp
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37
38#ifndef PCL_ISS_KEYPOINT3D_IMPL_H_
39#define PCL_ISS_KEYPOINT3D_IMPL_H_
40
41#include <Eigen/Eigenvalues> // for SelfAdjointEigenSolver
42#include <pcl/features/boundary.h>
43#include <pcl/features/normal_3d.h>
44#include <pcl/features/integral_image_normal.h>
45
46#include <pcl/keypoints/iss_3d.h>
47
48//////////////////////////////////////////////////////////////////////////////////////////////
49template<typename PointInT, typename PointOutT, typename NormalT> void
51{
52 salient_radius_ = salient_radius;
53}
54
55//////////////////////////////////////////////////////////////////////////////////////////////
56template<typename PointInT, typename PointOutT, typename NormalT> void
58{
59 non_max_radius_ = non_max_radius;
60}
61
62//////////////////////////////////////////////////////////////////////////////////////////////
63template<typename PointInT, typename PointOutT, typename NormalT> void
65{
66 normal_radius_ = normal_radius;
67}
68
69//////////////////////////////////////////////////////////////////////////////////////////////
70template<typename PointInT, typename PointOutT, typename NormalT> void
72{
73 border_radius_ = border_radius;
74}
75
76//////////////////////////////////////////////////////////////////////////////////////////////
77template<typename PointInT, typename PointOutT, typename NormalT> void
79{
80 gamma_21_ = gamma_21;
81}
82
83//////////////////////////////////////////////////////////////////////////////////////////////
84template<typename PointInT, typename PointOutT, typename NormalT> void
86{
87 gamma_32_ = gamma_32;
88}
89
90//////////////////////////////////////////////////////////////////////////////////////////////
91template<typename PointInT, typename PointOutT, typename NormalT> void
93{
94 min_neighbors_ = min_neighbors;
95}
96
97//////////////////////////////////////////////////////////////////////////////////////////////
98template<typename PointInT, typename PointOutT, typename NormalT> void
100{
101 normals_ = normals;
102}
103
104//////////////////////////////////////////////////////////////////////////////////////////////
105template<typename PointInT, typename PointOutT, typename NormalT> bool*
107{
108 bool* edge_points = new bool [input.size ()];
109
110 Eigen::Vector4f u = Eigen::Vector4f::Zero ();
111 Eigen::Vector4f v = Eigen::Vector4f::Zero ();
112
114 boundary_estimator.setInputCloud (input_);
115
116#pragma omp parallel for \
117 default(none) \
118 shared(angle_threshold, boundary_estimator, border_radius, edge_points, input) \
119 firstprivate(u, v) \
120 num_threads(threads_)
121 for (int index = 0; index < int (input.size ()); index++)
122 {
123 edge_points[index] = false;
124 PointInT current_point = input[index];
125
126 if (pcl::isFinite(current_point))
127 {
128 pcl::Indices nn_indices;
129 std::vector<float> nn_distances;
130 int n_neighbors;
131
132 this->searchForNeighbors (index, border_radius, nn_indices, nn_distances);
133
134 n_neighbors = static_cast<int> (nn_indices.size ());
135
136 if (n_neighbors >= min_neighbors_)
137 {
138 boundary_estimator.getCoordinateSystemOnPlane ((*normals_)[index], u, v);
139
140 if (boundary_estimator.isBoundaryPoint (input, static_cast<int> (index), nn_indices, u, v, angle_threshold))
141 edge_points[index] = true;
142 }
143 }
144 }
145
146 return (edge_points);
147}
148
149//////////////////////////////////////////////////////////////////////////////////////////////
150template<typename PointInT, typename PointOutT, typename NormalT> void
151pcl::ISSKeypoint3D<PointInT, PointOutT, NormalT>::getScatterMatrix (const int& current_index, Eigen::Matrix3d &cov_m)
152{
153 const PointInT& current_point = (*input_)[current_index];
154
155 double central_point[3];
156 memset(central_point, 0, sizeof(double) * 3);
157
158 central_point[0] = current_point.x;
159 central_point[1] = current_point.y;
160 central_point[2] = current_point.z;
161
162 cov_m = Eigen::Matrix3d::Zero ();
163
164 pcl::Indices nn_indices;
165 std::vector<float> nn_distances;
166 int n_neighbors;
167
168 this->searchForNeighbors (current_index, salient_radius_, nn_indices, nn_distances);
169
170 n_neighbors = static_cast<int> (nn_indices.size ());
171
172 if (n_neighbors < min_neighbors_)
173 return;
174
175 double cov[9];
176 memset(cov, 0, sizeof(double) * 9);
177
178 for (const auto& n_idx : nn_indices)
179 {
180 const PointInT& n_point = (*input_)[n_idx];
181
182 double neigh_point[3];
183 memset(neigh_point, 0, sizeof(double) * 3);
184
185 neigh_point[0] = n_point.x;
186 neigh_point[1] = n_point.y;
187 neigh_point[2] = n_point.z;
188
189 for (int i = 0; i < 3; i++)
190 for (int j = 0; j < 3; j++)
191 cov[i * 3 + j] += (neigh_point[i] - central_point[i]) * (neigh_point[j] - central_point[j]);
192 }
193
194 cov_m << cov[0], cov[1], cov[2],
195 cov[3], cov[4], cov[5],
196 cov[6], cov[7], cov[8];
197}
198
199//////////////////////////////////////////////////////////////////////////////////////////////
200template<typename PointInT, typename PointOutT, typename NormalT> bool
202{
204 {
205 PCL_ERROR ("[pcl::%s::initCompute] init failed!\n", name_.c_str ());
206 return (false);
207 }
208 if (salient_radius_ <= 0)
209 {
210 PCL_ERROR ("[pcl::%s::initCompute] : the salient radius (%f) must be strict positive!\n",
211 name_.c_str (), salient_radius_);
212 return (false);
213 }
214 if (non_max_radius_ <= 0)
215 {
216 PCL_ERROR ("[pcl::%s::initCompute] : the non maxima radius (%f) must be strict positive!\n",
217 name_.c_str (), non_max_radius_);
218 return (false);
219 }
220 if (gamma_21_ <= 0)
221 {
222 PCL_ERROR ("[pcl::%s::initCompute] : the threshold on the ratio between the 2nd and the 1rst eigenvalue (%f) must be strict positive!\n",
223 name_.c_str (), gamma_21_);
224 return (false);
225 }
226 if (gamma_32_ <= 0)
227 {
228 PCL_ERROR ("[pcl::%s::initCompute] : the threshold on the ratio between the 3rd and the 2nd eigenvalue (%f) must be strict positive!\n",
229 name_.c_str (), gamma_32_);
230 return (false);
231 }
232 if (min_neighbors_ <= 0)
233 {
234 PCL_ERROR ("[pcl::%s::initCompute] : the minimum number of neighbors (%f) must be strict positive!\n",
235 name_.c_str (), min_neighbors_);
236 return (false);
237 }
238
239 delete[] third_eigen_value_;
240
241 third_eigen_value_ = new double[input_->size ()];
242 memset(third_eigen_value_, 0, sizeof(double) * input_->size ());
243
244 delete[] edge_points_;
245
246 if (border_radius_ > 0.0)
247 {
248 if (normals_->empty ())
249 {
250 if (normal_radius_ <= 0.)
251 {
252 PCL_ERROR ("[pcl::%s::initCompute] : the radius used to estimate surface normals (%f) must be positive!\n",
253 name_.c_str (), normal_radius_);
254 return (false);
255 }
256
257 PointCloudNPtr normal_ptr (new PointCloudN ());
258 if (input_->height == 1 )
259 {
261 normal_estimation.setInputCloud (surface_);
262 normal_estimation.setRadiusSearch (normal_radius_);
263 normal_estimation.compute (*normal_ptr);
264 }
265 else
266 {
269 normal_estimation.setInputCloud (surface_);
270 normal_estimation.setNormalSmoothingSize (5.0);
271 normal_estimation.compute (*normal_ptr);
272 }
273 normals_ = normal_ptr;
274 }
275 if (normals_->size () != surface_->size ())
276 {
277 PCL_ERROR ("[pcl::%s::initCompute] normals given, but the number of normals does not match the number of input points!\n", name_.c_str ());
278 return (false);
279 }
280 }
281 else if (border_radius_ < 0.0)
282 {
283 PCL_ERROR ("[pcl::%s::initCompute] : the border radius used to estimate boundary points (%f) must be positive!\n",
284 name_.c_str (), border_radius_);
285 return (false);
286 }
287
288 return (true);
289}
290
291//////////////////////////////////////////////////////////////////////////////////////////////
292template<typename PointInT, typename PointOutT, typename NormalT> void
294{
295 // Make sure the output cloud is empty
296 output.clear ();
297
298 if (border_radius_ > 0.0)
299 edge_points_ = getBoundaryPoints (*(input_->makeShared ()), border_radius_, angle_threshold_);
300
301 bool* borders = new bool [input_->size()];
302
303#pragma omp parallel for \
304 default(none) \
305 shared(borders) \
306 num_threads(threads_)
307 for (int index = 0; index < int (input_->size ()); index++)
308 {
309 borders[index] = false;
310 PointInT current_point = (*input_)[index];
311
312 if ((border_radius_ > 0.0) && (pcl::isFinite(current_point)))
313 {
314 pcl::Indices nn_indices;
315 std::vector<float> nn_distances;
316
317 this->searchForNeighbors (index, border_radius_, nn_indices, nn_distances);
318
319 for (const auto &nn_index : nn_indices)
320 {
321 if (edge_points_[nn_index])
322 {
323 borders[index] = true;
324 break;
325 }
326 }
327 }
328 }
329
330#ifdef _OPENMP
331 Eigen::Vector3d *omp_mem = new Eigen::Vector3d[threads_];
332
333 for (std::size_t i = 0; i < threads_; i++)
334 omp_mem[i].setZero (3);
335#else
336 Eigen::Vector3d *omp_mem = new Eigen::Vector3d[1];
337
338 omp_mem[0].setZero (3);
339#endif
340
341 double *prg_local_mem = new double[input_->size () * 3];
342 double **prg_mem = new double * [input_->size ()];
343
344 for (std::size_t i = 0; i < input_->size (); i++)
345 prg_mem[i] = prg_local_mem + 3 * i;
346
347#pragma omp parallel for \
348 default(none) \
349 shared(borders, omp_mem, prg_mem) \
350 num_threads(threads_)
351 for (int index = 0; index < static_cast<int> (input_->size ()); index++)
352 {
353#ifdef _OPENMP
354 int tid = omp_get_thread_num ();
355#else
356 int tid = 0;
357#endif
358 PointInT current_point = (*input_)[index];
359
360 if ((!borders[index]) && pcl::isFinite(current_point))
361 {
362 //if the considered point is not a border point and the point is "finite", then compute the scatter matrix
363 Eigen::Matrix3d cov_m = Eigen::Matrix3d::Zero ();
364 getScatterMatrix (static_cast<int> (index), cov_m);
365
366 Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> solver (cov_m);
367
368 const double& e1c = solver.eigenvalues ()[2];
369 const double& e2c = solver.eigenvalues ()[1];
370 const double& e3c = solver.eigenvalues ()[0];
371
372 if (!std::isfinite (e1c) || !std::isfinite (e2c) || !std::isfinite (e3c))
373 continue;
374
375 if (e3c < 0)
376 {
377 PCL_WARN ("[pcl::%s::detectKeypoints] : The third eigenvalue is negative! Skipping the point with index %i.\n",
378 name_.c_str (), index);
379 continue;
380 }
381
382 omp_mem[tid][0] = e2c / e1c;
383 omp_mem[tid][1] = e3c / e2c;;
384 omp_mem[tid][2] = e3c;
385 }
386
387 for (Eigen::Index d = 0; d < omp_mem[tid].size (); d++)
388 prg_mem[index][d] = omp_mem[tid][d];
389 }
390
391 for (int index = 0; index < int (input_->size ()); index++)
392 {
393 if (!borders[index])
394 {
395 if ((prg_mem[index][0] < gamma_21_) && (prg_mem[index][1] < gamma_32_))
396 third_eigen_value_[index] = prg_mem[index][2];
397 }
398 }
399
400 bool* feat_max = new bool [input_->size()];
401
402#pragma omp parallel for \
403 default(none) \
404 shared(feat_max) \
405 num_threads(threads_)
406 for (int index = 0; index < int (input_->size ()); index++)
407 {
408 feat_max [index] = false;
409 PointInT current_point = (*input_)[index];
410
411 if ((third_eigen_value_[index] > 0.0) && (pcl::isFinite(current_point)))
412 {
413 pcl::Indices nn_indices;
414 std::vector<float> nn_distances;
415 int n_neighbors;
416
417 this->searchForNeighbors (index, non_max_radius_, nn_indices, nn_distances);
418
419 n_neighbors = static_cast<int> (nn_indices.size ());
420
421 if (n_neighbors >= min_neighbors_)
422 {
423 bool is_max = true;
424
425 for (const auto& j : nn_indices)
426 if (third_eigen_value_[index] < third_eigen_value_[j])
427 is_max = false;
428 if (is_max)
429 feat_max[index] = true;
430 }
431 }
432 }
433
434#pragma omp parallel for \
435 default(none) \
436 shared(feat_max, output) \
437 num_threads(threads_)
438 for (int index = 0; index < int (input_->size ()); index++)
439 {
440 if (feat_max[index])
441#pragma omp critical
442 {
443 PointOutT p;
444 p.getVector3fMap () = (*input_)[index].getVector3fMap ();
445 output.push_back(p);
446 keypoints_indices_->indices.push_back (index);
447 }
448 }
449
450 output.header = input_->header;
451 output.width = output.size ();
452 output.height = 1;
453
454 // Clear the contents of variables and arrays before the beginning of the next computation.
455 if (border_radius_ > 0.0)
456 normals_.reset (new pcl::PointCloud<NormalT>);
457
458 delete[] borders;
459 delete[] prg_mem;
460 delete[] prg_local_mem;
461 delete[] feat_max;
462 delete[] omp_mem;
463}
464
465#define PCL_INSTANTIATE_ISSKeypoint3D(T,U,N) template class PCL_EXPORTS pcl::ISSKeypoint3D<T,U,N>;
466
467#endif /* PCL_ISS_3D_IMPL_H_ */
BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle cr...
Definition: boundary.h:80
void getCoordinateSystemOnPlane(const PointNT &p_coeff, Eigen::Vector4f &u, Eigen::Vector4f &v)
Get a u-v-n coordinate system that lies on a plane defined by its normal.
Definition: boundary.h:159
bool isBoundaryPoint(const pcl::PointCloud< PointInT > &cloud, int q_idx, const pcl::Indices &indices, const Eigen::Vector4f &u, const Eigen::Vector4f &v, const float angle_threshold)
Check whether a point is a boundary point in a planar patch of projected points given by indices.
Definition: boundary.hpp:51
void setRadiusSearch(double radius)
Set the sphere radius that is to be used for determining the nearest neighbors used for the feature e...
Definition: feature.h:201
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition: feature.hpp:194
typename PointCloudN::ConstPtr PointCloudNConstPtr
Definition: iss_3d.h:96
typename Keypoint< PointInT, PointOutT >::PointCloudIn PointCloudIn
Definition: iss_3d.h:91
void setNormals(const PointCloudNConstPtr &normals)
Set the normals if pre-calculated normals are available.
Definition: iss_3d.hpp:99
void setBorderRadius(double border_radius)
Set the radius used for the estimation of the boundary points.
Definition: iss_3d.hpp:71
void setSalientRadius(double salient_radius)
Set the radius of the spherical neighborhood used to compute the scatter matrix.
Definition: iss_3d.hpp:50
void getScatterMatrix(const int &current_index, Eigen::Matrix3d &cov_m)
Compute the scatter matrix for a point index.
Definition: iss_3d.hpp:151
void setThreshold21(double gamma_21)
Set the upper bound on the ratio between the second and the first eigenvalue.
Definition: iss_3d.hpp:78
void setMinNeighbors(int min_neighbors)
Set the minimum number of neighbors that has to be found while applying the non maxima suppression al...
Definition: iss_3d.hpp:92
void setNormalRadius(double normal_radius)
Set the radius used for the estimation of the surface normals of the input cloud.
Definition: iss_3d.hpp:64
void setThreshold32(double gamma_32)
Set the upper bound on the ratio between the third and the second eigenvalue.
Definition: iss_3d.hpp:85
bool initCompute() override
Perform the initial checks before computing the keypoints.
Definition: iss_3d.hpp:201
typename PointCloudN::Ptr PointCloudNPtr
Definition: iss_3d.h:95
void setNonMaxRadius(double non_max_radius)
Set the radius for the application of the non maxima supression algorithm.
Definition: iss_3d.hpp:57
bool * getBoundaryPoints(PointCloudIn &input, double border_radius, float angle_threshold)
Compute the boundary points for the given input cloud.
Definition: iss_3d.hpp:106
void detectKeypoints(PointCloudOut &output) override
Detect the keypoints by performing the EVD of the scatter matrix.
Definition: iss_3d.hpp:293
typename Keypoint< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: iss_3d.h:92
Surface normal estimation on organized data using integral images.
void setNormalEstimationMethod(NormalEstimationMethod normal_estimation_method)
Set the normal estimation method.
void setInputCloud(const typename PointCloudIn::ConstPtr &cloud) override
Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
void setNormalSmoothingSize(float normal_smoothing_size)
Set the normal smoothing size.
Keypoint represents the base class for key points.
Definition: keypoint.h:49
NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point.
Definition: normal_3d.h:244
void setInputCloud(const PointCloudConstPtr &cloud) override
Provide a pointer to the input dataset.
Definition: normal_3d.h:332
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: pcl_base.hpp:65
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition: point_tests.h:55
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133