Point Cloud Library (PCL) 1.15.0
Loading...
Searching...
No Matches
mls.h
1/*
2 * Software License Agreement (BSD License)
3 *
4 * Point Cloud Library (PCL) - www.pointclouds.org
5 * Copyright (c) 2009-2011, Willow Garage, Inc.
6 *
7 * All rights reserved.
8 *
9 * Redistribution and use in source and binary forms, with or without
10 * modification, are permitted provided that the following conditions
11 * are met:
12 *
13 * * Redistributions of source code must retain the above copyright
14 * notice, this list of conditions and the following disclaimer.
15 * * Redistributions in binary form must reproduce the above
16 * copyright notice, this list of conditions and the following
17 * disclaimer in the documentation and/or other materials provided
18 * with the distribution.
19 * * Neither the name of Willow Garage, Inc. nor the names of its
20 * contributors may be used to endorse or promote products derived
21 * from this software without specific prior written permission.
22 *
23 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
24 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
25 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
26 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
27 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
28 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
29 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
30 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
31 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
32 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
33 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
34 * POSSIBILITY OF SUCH DAMAGE.
35 *
36 * $Id$
37 *
38 */
39
40#pragma once
41
42#include <functional>
43#include <map>
44#include <random>
45#include <Eigen/Core> // for Vector3i, Vector3d, ...
46
47// PCL includes
48#include <pcl/memory.h>
49#include <pcl/pcl_base.h>
50#include <pcl/pcl_macros.h>
51#include <pcl/search/search.h> // for Search
52
53#include <pcl/surface/processing.h>
54
55namespace pcl
56{
57
58 /** \brief Data structure used to store the results of the MLS fitting */
59 struct MLSResult
60 {
62 {
63 NONE, /**< \brief Project to the mls plane. */
64 SIMPLE, /**< \brief Project along the mls plane normal to the polynomial surface. */
65 ORTHOGONAL /**< \brief Project to the closest point on the polynonomial surface. */
66 };
67
68 /** \brief Data structure used to store the MLS polynomial partial derivatives */
70 {
71 double z; /**< \brief The z component of the polynomial evaluated at z(u, v). */
72 double z_u; /**< \brief The partial derivative dz/du. */
73 double z_v; /**< \brief The partial derivative dz/dv. */
74 double z_uu; /**< \brief The partial derivative d^2z/du^2. */
75 double z_vv; /**< \brief The partial derivative d^2z/dv^2. */
76 double z_uv; /**< \brief The partial derivative d^2z/dudv. */
77 };
78
79 /** \brief Data structure used to store the MLS projection results */
81 {
83
84 double u{0.0}; /**< \brief The u-coordinate of the projected point in local MLS frame. */
85 double v{0.0}; /**< \brief The v-coordinate of the projected point in local MLS frame. */
86 Eigen::Vector3d point; /**< \brief The projected point. */
87 Eigen::Vector3d normal; /**< \brief The projected point's normal. */
89 };
90
91 inline
92 MLSResult () : num_neighbors (0), curvature (0.0f), order (0), valid (false) {}
93
94 inline
95 MLSResult (const Eigen::Vector3d &a_query_point,
96 const Eigen::Vector3d &a_mean,
97 const Eigen::Vector3d &a_plane_normal,
98 const Eigen::Vector3d &a_u,
99 const Eigen::Vector3d &a_v,
100 const Eigen::VectorXd &a_c_vec,
101 const int a_num_neighbors,
102 const float a_curvature,
103 const int a_order);
104
105 /** \brief Given a point calculate its 3D location in the MLS frame.
106 * \param[in] pt The point
107 * \param[out] u The u-coordinate of the point in local MLS frame.
108 * \param[out] v The v-coordinate of the point in local MLS frame.
109 * \param[out] w The w-coordinate of the point in local MLS frame.
110 */
111 inline void
112 getMLSCoordinates (const Eigen::Vector3d &pt, double &u, double &v, double &w) const;
113
114 /** \brief Given a point calculate its 2D location in the MLS frame.
115 * \param[in] pt The point
116 * \param[out] u The u-coordinate of the point in local MLS frame.
117 * \param[out] v The v-coordinate of the point in local MLS frame.
118 */
119 inline void
120 getMLSCoordinates (const Eigen::Vector3d &pt, double &u, double &v) const;
121
122 /** \brief Calculate the polynomial
123 * \param[in] u The u-coordinate of the point in local MLS frame.
124 * \param[in] v The v-coordinate of the point in local MLS frame.
125 * \return The polynomial value at the provided uv coordinates.
126 */
127 inline double
128 getPolynomialValue (const double u, const double v) const;
129
130 /** \brief Calculate the polynomial's first and second partial derivatives.
131 * \param[in] u The u-coordinate of the point in local MLS frame.
132 * \param[in] v The v-coordinate of the point in local MLS frame.
133 * \return The polynomial partial derivatives at the provided uv coordinates.
134 */
135 inline PolynomialPartialDerivative
136 getPolynomialPartialDerivative (const double u, const double v) const;
137
138 /** \brief Calculate the principal curvatures using the polynomial surface.
139 * \param[in] u The u-coordinate of the point in local MLS frame.
140 * \param[in] v The v-coordinate of the point in local MLS frame.
141 * \return The principal curvature [k1, k2] at the provided uv coordinates.
142 * \note If an error occurs then 1e-5 is returned.
143 */
144 Eigen::Vector2f
145 calculatePrincipalCurvatures (const double u, const double v) const;
146
147 /** \brief Project a point orthogonal to the polynomial surface.
148 * \param[in] u The u-coordinate of the point in local MLS frame.
149 * \param[in] v The v-coordinate of the point in local MLS frame.
150 * \param[in] w The w-coordinate of the point in local MLS frame.
151 * \return The MLSProjectionResults for the input data.
152 * \note If the MLSResults does not contain polynomial data it projects the point onto the mls plane.
153 * \note If the optimization diverges it performs a simple projection on to the polynomial surface.
154 * \note This was implemented based on this https://math.stackexchange.com/questions/1497093/shortest-distance-between-point-and-surface
155 */
157 projectPointOrthogonalToPolynomialSurface (const double u, const double v, const double w) const;
158
159 /** \brief Project a point onto the MLS plane.
160 * \param[in] u The u-coordinate of the point in local MLS frame.
161 * \param[in] v The v-coordinate of the point in local MLS frame.
162 * \return The MLSProjectionResults for the input data.
163 */
165 projectPointToMLSPlane (const double u, const double v) const;
166
167 /** \brief Project a point along the MLS plane normal to the polynomial surface.
168 * \param[in] u The u-coordinate of the point in local MLS frame.
169 * \param[in] v The v-coordinate of the point in local MLS frame.
170 * \return The MLSProjectionResults for the input data.
171 * \note If the MLSResults does not contain polynomial data it projects the point onto the mls plane.
172 */
174 projectPointSimpleToPolynomialSurface (const double u, const double v) const;
175
176 /**
177 * \brief Project a point using the specified method.
178 * \param[in] pt The point to be project.
179 * \param[in] method The projection method to be used.
180 * \param[in] required_neighbors The minimum number of neighbors required.
181 * \note If required_neighbors is 0 then any number of neighbors is allowed.
182 * \note If required_neighbors is not satisfied it projects to the mls plane.
183 * \return The MLSProjectionResults for the input data.
184 */
186 projectPoint (const Eigen::Vector3d &pt, ProjectionMethod method, int required_neighbors = 0) const;
187
188 /**
189 * \brief Project the query point used to generate the mls surface about using the specified method.
190 * \param[in] method The projection method to be used.
191 * \param[in] required_neighbors The minimum number of neighbors required.
192 * \note If required_neighbors is 0 then any number of neighbors is allowed.
193 * \note If required_neighbors is not satisfied it projects to the mls plane.
194 * \return The MLSProjectionResults for the input data.
195 */
197 projectQueryPoint (ProjectionMethod method, int required_neighbors = 0) const;
198
199 /** \brief Smooth a given point and its neighborhood using Moving Least Squares.
200 * \param[in] cloud the input cloud, used together with index and nn_indices
201 * \param[in] index the index of the query point in the input cloud
202 * \param[in] nn_indices the set of nearest neighbors indices for pt
203 * \param[in] search_radius the search radius used to find nearest neighbors for pt
204 * \param[in] polynomial_order the order of the polynomial to fit to the nearest neighbors
205 * \param[in] weight_func defines the weight function for the polynomial fit
206 */
207 template <typename PointT> void
209 pcl::index_t index,
210 const pcl::Indices &nn_indices,
211 double search_radius,
212 int polynomial_order = 2,
213 std::function<double(const double)> weight_func = {});
214
215 Eigen::Vector3d query_point; /**< \brief The query point about which the mls surface was generated */
216 Eigen::Vector3d mean; /**< \brief The mean point of all the neighbors. */
217 Eigen::Vector3d plane_normal; /**< \brief The normal of the local plane of the query point. */
218 Eigen::Vector3d u_axis; /**< \brief The axis corresponding to the u-coordinates of the local plane of the query point. */
219 Eigen::Vector3d v_axis; /**< \brief The axis corresponding to the v-coordinates of the local plane of the query point. */
220 Eigen::VectorXd c_vec; /**< \brief The polynomial coefficients Example: z = c_vec[0] + c_vec[1]*v + c_vec[2]*v^2 + c_vec[3]*u + c_vec[4]*u*v + c_vec[5]*u^2 */
221 int num_neighbors; /**< \brief The number of neighbors used to create the mls surface. */
222 float curvature; /**< \brief The curvature at the query point. */
223 int order; /**< \brief The order of the polynomial. If order > 1 then use polynomial fit */
224 bool valid; /**< \brief If True, the mls results data is valid, otherwise False. */
226 private:
227 /**
228 * \brief The default weight function used when fitting a polynomial surface
229 * \param sq_dist the squared distance from a point to origin of the mls frame
230 * \param sq_mls_radius the squraed mls search radius used
231 * \return The weight for a point at squared distance from the origin of the mls frame
232 */
233 inline
234 double computeMLSWeight (const double sq_dist, const double sq_mls_radius) { return (std::exp (-sq_dist / sq_mls_radius)); }
235
236 };
237
238 /** \brief MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm
239 * for data smoothing and improved normal estimation. It also contains methods for upsampling the
240 * resulting cloud based on the parametric fit.
241 * Reference paper: "Computing and Rendering Point Set Surfaces" by Marc Alexa, Johannes Behr,
242 * Daniel Cohen-Or, Shachar Fleishman, David Levin and Claudio T. Silva
243 * www.sci.utah.edu/~shachar/Publications/crpss.pdf
244 * \note There is a parallelized version of the processing step, using the OpenMP standard.
245 * Compared to the standard version, an overhead is incurred in terms of runtime and memory usage.
246 * The upsampling methods DISTINCT_CLOUD and VOXEL_GRID_DILATION are not parallelized completely,
247 * i.e. parts of the algorithm run on a single thread only.
248 * \author Zoltan Csaba Marton, Radu B. Rusu, Alexandru E. Ichim, Suat Gedikli, Robert Huitl
249 * \ingroup surface
250 */
251 template <typename PointInT, typename PointOutT>
252 class MovingLeastSquares : public CloudSurfaceProcessing<PointInT, PointOutT>
253 {
254 public:
255 using Ptr = shared_ptr<MovingLeastSquares<PointInT, PointOutT> >;
256 using ConstPtr = shared_ptr<const MovingLeastSquares<PointInT, PointOutT> >;
257
258 using PCLBase<PointInT>::input_;
259 using PCLBase<PointInT>::indices_;
260 using PCLBase<PointInT>::fake_indices_;
261 using PCLBase<PointInT>::initCompute;
262 using PCLBase<PointInT>::deinitCompute;
263
265 using KdTreePtr = typename KdTree::Ptr;
268
272
276
277 using SearchMethod = std::function<int (pcl::index_t, double, pcl::Indices &, std::vector<float> &)>;
278
280 {
281 NONE, /**< \brief No upsampling will be done, only the input points will be projected
282 to their own MLS surfaces. */
283 DISTINCT_CLOUD, /**< \brief Project the points of the distinct cloud to the MLS surface. */
284 SAMPLE_LOCAL_PLANE, /**< \brief The local plane of each input point will be sampled in a circular fashion
285 using the \ref upsampling_radius_ and the \ref upsampling_step_ parameters. */
286 RANDOM_UNIFORM_DENSITY, /**< \brief The local plane of each input point will be sampled using an uniform random
287 distribution such that the density of points is constant throughout the
288 cloud - given by the \ref desired_num_points_in_radius_ parameter. */
289 VOXEL_GRID_DILATION /**< \brief The input cloud will be inserted into a voxel grid with voxels of
290 size \ref voxel_size_; this voxel grid will be dilated \ref dilation_iteration_num_
291 times and the resulting points will be projected to the MLS surface
292 of the closest point in the input cloud; the result is a point cloud
293 with filled holes and a constant point density. */
294 };
295
296 /** \brief Empty constructor. */
297 MovingLeastSquares () : CloudSurfaceProcessing<PointInT, PointOutT> (),
299 tree_ (),
300
302
303 rng_uniform_distribution_ ()
304 {};
305
306 /** \brief Empty destructor */
307 ~MovingLeastSquares () override = default;
308
309
310 /** \brief Set whether the algorithm should also store the normals computed
311 * \note This is optional, but need a proper output cloud type
312 */
313 inline void
314 setComputeNormals (bool compute_normals) { compute_normals_ = compute_normals; }
315
316 /** \brief Provide a pointer to the search object.
317 * \param[in] tree a pointer to the spatial search object.
318 */
319 inline void
321 {
322 tree_ = tree;
323 // Declare the search locator definition
324 search_method_ = [this] (pcl::index_t index, double radius, pcl::Indices& k_indices, std::vector<float>& k_sqr_distances)
325 {
326 return tree_->radiusSearch (index, radius, k_indices, k_sqr_distances, 0);
327 };
328 }
329
330 /** \brief Get a pointer to the search method used. */
331 inline KdTreePtr
332 getSearchMethod () const { return (tree_); }
333
334 /** \brief Set the order of the polynomial to be fit.
335 * \param[in] order the order of the polynomial
336 * \note Setting order > 1 indicates using a polynomial fit.
337 */
338 inline void
339 setPolynomialOrder (int order) { order_ = order; }
340
341 /** \brief Get the order of the polynomial to be fit. */
342 inline int
343 getPolynomialOrder () const { return (order_); }
344
345 /** \brief Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting.
346 * \param[in] radius the sphere radius that is to contain all k-nearest neighbors
347 * \note Calling this method resets the squared Gaussian parameter to radius * radius !
348 */
349 inline void
351
352 /** \brief Get the sphere radius used for determining the k-nearest neighbors. */
353 inline double
354 getSearchRadius () const { return (search_radius_); }
355
356 /** \brief Set the parameter used for distance based weighting of neighbors (the square of the search radius works
357 * best in general).
358 * \param[in] sqr_gauss_param the squared Gaussian parameter
359 */
360 inline void
361 setSqrGaussParam (double sqr_gauss_param) { sqr_gauss_param_ = sqr_gauss_param; }
362
363 /** \brief Get the parameter for distance based weighting of neighbors. */
364 inline double
365 getSqrGaussParam () const { return (sqr_gauss_param_); }
366
367 /** \brief Set the upsampling method to be used
368 * \param method
369 */
370 inline void
372
373 /** \brief Set the distinct cloud used for the DISTINCT_CLOUD upsampling method. */
374 inline void
375 setDistinctCloud (PointCloudInConstPtr distinct_cloud) { distinct_cloud_ = distinct_cloud; }
376
377 /** \brief Get the distinct cloud used for the DISTINCT_CLOUD upsampling method. */
379 getDistinctCloud () const { return (distinct_cloud_); }
380
381
382 /** \brief Set the radius of the circle in the local point plane that will be sampled
383 * \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
384 * \param[in] radius the radius of the circle
385 */
386 inline void
387 setUpsamplingRadius (double radius) { upsampling_radius_ = radius; }
388
389 /** \brief Get the radius of the circle in the local point plane that will be sampled
390 * \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
391 */
392 inline double
394
395 /** \brief Set the step size for the local plane sampling
396 * \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
397 * \param[in] step_size the step size
398 */
399 inline void
400 setUpsamplingStepSize (double step_size) { upsampling_step_ = step_size; }
401
402
403 /** \brief Get the step size for the local plane sampling
404 * \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
405 */
406 inline double
408
409 /** \brief Set the parameter that specifies the desired number of points within the search radius
410 * \note Used only in the case of RANDOM_UNIFORM_DENSITY upsampling
411 * \param[in] desired_num_points_in_radius the desired number of points in the output cloud in a sphere of
412 * radius \ref search_radius_ around each point
413 */
414 inline void
415 setPointDensity (int desired_num_points_in_radius) { desired_num_points_in_radius_ = desired_num_points_in_radius; }
416
417
418 /** \brief Get the parameter that specifies the desired number of points within the search radius
419 * \note Used only in the case of RANDOM_UNIFORM_DENSITY upsampling
420 */
421 inline int
423
424 /** \brief Set the voxel size for the voxel grid
425 * \note Used only in the VOXEL_GRID_DILATION upsampling method
426 * \param[in] voxel_size the edge length of a cubic voxel in the voxel grid
427 */
428 inline void
429 setDilationVoxelSize (float voxel_size) { voxel_size_ = voxel_size; }
430
431
432 /** \brief Get the voxel size for the voxel grid
433 * \note Used only in the VOXEL_GRID_DILATION upsampling method
434 */
435 inline float
436 getDilationVoxelSize () const { return (voxel_size_); }
437
438 /** \brief Set the number of dilation steps of the voxel grid
439 * \note Used only in the VOXEL_GRID_DILATION upsampling method
440 * \param[in] iterations the number of dilation iterations
441 */
442 inline void
443 setDilationIterations (int iterations) { dilation_iteration_num_ = iterations; }
444
445 /** \brief Get the number of dilation steps of the voxel grid
446 * \note Used only in the VOXEL_GRID_DILATION upsampling method
447 */
448 inline int
450
451 /** \brief Set whether the mls results should be stored for each point in the input cloud
452 * \param[in] cache_mls_results True if the mls results should be stored, otherwise false.
453 * \note The cache_mls_results_ is forced to be true when using upsampling method VOXEL_GRID_DILATION or DISTINCT_CLOUD.
454 * \note If memory consumption is a concern, then set it to false when not using upsampling method VOXEL_GRID_DILATION or DISTINCT_CLOUD.
455 */
456 inline void
457 setCacheMLSResults (bool cache_mls_results) { cache_mls_results_ = cache_mls_results; }
458
459 /** \brief Get the cache_mls_results_ value (True if the mls results should be stored, otherwise false). */
460 inline bool
462
463 /** \brief Set the method to be used when projection the point on to the MLS surface.
464 * \param method
465 * \note This is only used when polynomial fit is enabled.
466 */
467 inline void
469
470
471 /** \brief Get the current projection method being used. */
474
475 /** \brief Get the MLSResults for input cloud
476 * \note The results are only stored if setCacheMLSResults(true) was called or when using the upsampling method DISTINCT_CLOUD or VOXEL_GRID_DILATION.
477 * \note This vector is aligned with the input cloud indices, so use getCorrespondingIndices to get the correct results when using output cloud indices.
478 */
479 inline const std::vector<MLSResult>&
480 getMLSResults () const { return (mls_results_); }
481
482 /** \brief Set the maximum number of threads to use
483 * \param threads the maximum number of hardware threads to use (0 sets the value to 1)
484 */
485 inline void
486 setNumberOfThreads (unsigned int threads = 1)
487 {
488 threads_ = threads;
489 }
490
491 /** \brief Base method for surface reconstruction for all points given in <setInputCloud (), setIndices ()>
492 * \param[out] output the resultant reconstructed surface model
493 */
494 void
495 process (PointCloudOut &output) override;
496
497
498 /** \brief Get the set of indices with each point in output having the
499 * corresponding point in input */
500 inline PointIndicesPtr
502
503 protected:
504 /** \brief The point cloud that will hold the estimated normals, if set. */
506
507 /** \brief The distinct point cloud that will be projected to the MLS surface. */
509
510 /** \brief The search method template for indices. */
512
513 /** \brief A pointer to the spatial search object. */
514 KdTreePtr tree_{nullptr};
515
516 /** \brief The order of the polynomial to be fit. */
517 int order_{2};
518
519 /** \brief The nearest neighbors search radius for each point. */
520 double search_radius_{0.0};
521
522 /** \brief Parameter for distance based weighting of neighbors (search_radius_ * search_radius_ works fine) */
523 double sqr_gauss_param_{0.0};
524
525 /** \brief Parameter that specifies whether the normals should be computed for the input cloud or not */
526 bool compute_normals_{false};
527
528 /** \brief Parameter that specifies the upsampling method to be used */
530
531 /** \brief Radius of the circle in the local point plane that will be sampled
532 * \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
533 */
535
536 /** \brief Step size for the local plane sampling
537 * \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
538 */
539 double upsampling_step_{0.0};
540
541 /** \brief Parameter that specifies the desired number of points within the search radius
542 * \note Used only in the case of RANDOM_UNIFORM_DENSITY upsampling
543 */
545
546 /** \brief True if the mls results for the input cloud should be stored
547 * \note This is forced to be true when using upsampling methods VOXEL_GRID_DILATION or DISTINCT_CLOUD.
548 */
550
551 /** \brief Stores the MLS result for each point in the input cloud
552 * \note Used only in the case of VOXEL_GRID_DILATION or DISTINCT_CLOUD upsampling
553 */
554 std::vector<MLSResult> mls_results_{};
555
556 /** \brief Parameter that specifies the projection method to be used. */
558
559 /** \brief The maximum number of threads the scheduler should use. */
560 unsigned int threads_{1};
561
562
563 /** \brief A minimalistic implementation of a voxel grid, necessary for the point cloud upsampling
564 * \note Used only in the case of VOXEL_GRID_DILATION upsampling
565 */
567 {
568 public:
569 struct Leaf { Leaf () = default; bool valid{true}; };
570
572 IndicesPtr &indices,
573 float voxel_size,
574 int dilation_iteration_num);
575
576 void
577 dilate ();
578
579 inline void
580 getIndexIn1D (const Eigen::Vector3i &index, std::uint64_t &index_1d) const
581 {
582 index_1d = index[0] * data_size_ * data_size_ +
583 index[1] * data_size_ + index[2];
584 }
585
586 inline void
587 getIndexIn3D (std::uint64_t index_1d, Eigen::Vector3i& index_3d) const
588 {
589 index_3d[0] = static_cast<Eigen::Vector3i::Scalar> (index_1d / (data_size_ * data_size_));
590 index_1d -= index_3d[0] * data_size_ * data_size_;
591 index_3d[1] = static_cast<Eigen::Vector3i::Scalar> (index_1d / data_size_);
592 index_1d -= index_3d[1] * data_size_;
593 index_3d[2] = static_cast<Eigen::Vector3i::Scalar> (index_1d);
594 }
595
596 inline void
597 getCellIndex (const Eigen::Vector3f &p, Eigen::Vector3i& index) const
598 {
599 for (int i = 0; i < 3; ++i)
600 index[i] = static_cast<Eigen::Vector3i::Scalar> ((p[i] - bounding_min_ (i)) / voxel_size_);
601 }
602
603 inline void
604 getPosition (const std::uint64_t &index_1d, Eigen::Vector3f &point) const
605 {
606 Eigen::Vector3i index_3d;
607 getIndexIn3D (index_1d, index_3d);
608 for (int i = 0; i < 3; ++i)
609 point[i] = static_cast<Eigen::Vector3f::Scalar> (index_3d[i]) * voxel_size_ + bounding_min_[i];
610 }
611
612 using HashMap = std::map<std::uint64_t, Leaf>;
614 Eigen::Vector4f bounding_min_, bounding_max_;
615 std::uint64_t data_size_{0};
618 };
619
620
621 /** \brief Voxel size for the VOXEL_GRID_DILATION upsampling method */
622 float voxel_size_{1.0f};
623
624 /** \brief Number of dilation steps for the VOXEL_GRID_DILATION upsampling method */
626
627 /** \brief Number of coefficients, to be computed from the requested order.*/
628 int nr_coeff_{0};
629
630 /** \brief Collects for each point in output the corresponding point in the input. */
632
633 /** \brief Search for the nearest neighbors of a given point using a radius search
634 * \param[in] index the index of the query point
635 * \param[out] indices the resultant vector of indices representing the neighbors within search_radius_
636 * \param[out] sqr_distances the resultant squared distances from the query point to the neighbors within search_radius_
637 */
638 inline int
639 searchForNeighbors (pcl::index_t index, pcl::Indices &indices, std::vector<float> &sqr_distances) const
640 {
641 return (search_method_ (index, search_radius_, indices, sqr_distances));
642 }
643
644 /** \brief Smooth a given point and its neighborghood using Moving Least Squares.
645 * \param[in] index the index of the query point in the input cloud
646 * \param[in] nn_indices the set of nearest neighbors indices for pt
647 * \param[out] projected_points the set of projected points around the query point
648 * (in the case of upsampling method NONE, only the query point projected to its own fitted surface will be returned,
649 * in the case of the other upsampling methods, multiple points will be returned)
650 * \param[out] projected_points_normals the normals corresponding to the projected points
651 * \param[out] corresponding_input_indices the set of indices with each point in output having the corresponding point in input
652 * \param[out] mls_result stores the MLS result for each point in the input cloud
653 * (used only in the case of VOXEL_GRID_DILATION or DISTINCT_CLOUD upsampling)
654 */
655 void
657 const pcl::Indices &nn_indices,
658 PointCloudOut &projected_points,
659 NormalCloud &projected_points_normals,
660 PointIndices &corresponding_input_indices,
661 MLSResult &mls_result) const;
662
663
664 /** \brief This is a helper function for adding projected points
665 * \param[in] index the index of the query point in the input cloud
666 * \param[in] point the projected point to be added
667 * \param[in] normal the projected point's normal to be added
668 * \param[in] curvature the projected point's curvature
669 * \param[out] projected_points the set of projected points around the query point
670 * \param[out] projected_points_normals the normals corresponding to the projected points
671 * \param[out] corresponding_input_indices the set of indices with each point in output having the corresponding point in input
672 */
673 void
675 const Eigen::Vector3d &point,
676 const Eigen::Vector3d &normal,
677 double curvature,
678 PointCloudOut &projected_points,
679 NormalCloud &projected_points_normals,
680 PointIndices &corresponding_input_indices) const;
681
682
683 void
684 copyMissingFields (const PointInT &point_in,
685 PointOutT &point_out) const;
686
687 /** \brief Abstract surface reconstruction method.
688 * \param[out] output the result of the reconstruction
689 */
690 void
691 performProcessing (PointCloudOut &output) override;
692
693 /** \brief Perform upsampling for the distinct-cloud and voxel-grid methods
694 * \param[out] output the result of the reconstruction
695 */
696 void
698
699 private:
700 /** \brief Random number generator algorithm. */
701 mutable std::mt19937 rng_;
702
703 /** \brief Random number generator using an uniform distribution of floats
704 * \note Used only in the case of RANDOM_UNIFORM_DENSITY upsampling
705 */
706 std::unique_ptr<std::uniform_real_distribution<>> rng_uniform_distribution_;
707
708 /** \brief Abstract class get name method. */
709 std::string
710 getClassName () const { return ("MovingLeastSquares"); }
711 };
712}
713
714#ifdef PCL_NO_PRECOMPILE
715#include <pcl/surface/impl/mls.hpp>
716#endif
CloudSurfaceProcessing represents the base class for algorithms that takes a point cloud as input and...
Definition processing.h:58
A minimalistic implementation of a voxel grid, necessary for the point cloud upsampling.
Definition mls.h:567
void getPosition(const std::uint64_t &index_1d, Eigen::Vector3f &point) const
Definition mls.h:604
void getIndexIn1D(const Eigen::Vector3i &index, std::uint64_t &index_1d) const
Definition mls.h:580
void getCellIndex(const Eigen::Vector3f &p, Eigen::Vector3i &index) const
Definition mls.h:597
void getIndexIn3D(std::uint64_t index_1d, Eigen::Vector3i &index_3d) const
Definition mls.h:587
std::map< std::uint64_t, Leaf > HashMap
Definition mls.h:612
MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data s...
Definition mls.h:253
void setSqrGaussParam(double sqr_gauss_param)
Set the parameter used for distance based weighting of neighbors (the square of the search radius wor...
Definition mls.h:361
void setDilationIterations(int iterations)
Set the number of dilation steps of the voxel grid.
Definition mls.h:443
bool getCacheMLSResults() const
Get the cache_mls_results_ value (True if the mls results should be stored, otherwise false).
Definition mls.h:461
double getSqrGaussParam() const
Get the parameter for distance based weighting of neighbors.
Definition mls.h:365
unsigned int threads_
The maximum number of threads the scheduler should use.
Definition mls.h:560
void performUpsampling(PointCloudOut &output)
Perform upsampling for the distinct-cloud and voxel-grid methods.
Definition mls.hpp:372
typename PointCloudIn::Ptr PointCloudInPtr
Definition mls.h:274
int order_
The order of the polynomial to be fit.
Definition mls.h:517
double getSearchRadius() const
Get the sphere radius used for determining the k-nearest neighbors.
Definition mls.h:354
typename KdTree::Ptr KdTreePtr
Definition mls.h:265
MLSResult::ProjectionMethod projection_method_
Parameter that specifies the projection method to be used.
Definition mls.h:557
typename PointCloudOut::Ptr PointCloudOutPtr
Definition mls.h:270
KdTreePtr getSearchMethod() const
Get a pointer to the search method used.
Definition mls.h:332
void setPolynomialOrder(int order)
Set the order of the polynomial to be fit.
Definition mls.h:339
int getPolynomialOrder() const
Get the order of the polynomial to be fit.
Definition mls.h:343
double getUpsamplingRadius() const
Get the radius of the circle in the local point plane that will be sampled.
Definition mls.h:393
double search_radius_
The nearest neighbors search radius for each point.
Definition mls.h:520
MovingLeastSquares()
Empty constructor.
Definition mls.h:297
pcl::PointCloud< PointOutT > PointCloudOut
Definition mls.h:269
double sqr_gauss_param_
Parameter for distance based weighting of neighbors (search_radius_ * search_radius_ works fine)
Definition mls.h:523
typename PointCloudIn::ConstPtr PointCloudInConstPtr
Definition mls.h:275
int getPointDensity() const
Get the parameter that specifies the desired number of points within the search radius.
Definition mls.h:422
std::function< int(pcl::index_t, double, pcl::Indices &, std::vector< float > &)> SearchMethod
Definition mls.h:277
float getDilationVoxelSize() const
Get the voxel size for the voxel grid.
Definition mls.h:436
KdTreePtr tree_
A pointer to the spatial search object.
Definition mls.h:514
void copyMissingFields(const PointInT &point_in, PointOutT &point_out) const
Definition mls.hpp:866
void setComputeNormals(bool compute_normals)
Set whether the algorithm should also store the normals computed.
Definition mls.h:314
void setPointDensity(int desired_num_points_in_radius)
Set the parameter that specifies the desired number of points within the search radius.
Definition mls.h:415
MLSResult::ProjectionMethod getProjectionMethod() const
Get the current projection method being used.
Definition mls.h:473
shared_ptr< MovingLeastSquares< PointInT, PointOutT > > Ptr
Definition mls.h:255
double getUpsamplingStepSize() const
Get the step size for the local plane sampling.
Definition mls.h:407
int getDilationIterations() const
Get the number of dilation steps of the voxel grid.
Definition mls.h:449
double upsampling_step_
Step size for the local plane sampling.
Definition mls.h:539
NormalCloud::Ptr NormalCloudPtr
Definition mls.h:267
void setUpsamplingRadius(double radius)
Set the radius of the circle in the local point plane that will be sampled.
Definition mls.h:387
NormalCloudPtr normals_
The point cloud that will hold the estimated normals, if set.
Definition mls.h:505
void setDistinctCloud(PointCloudInConstPtr distinct_cloud)
Set the distinct cloud used for the DISTINCT_CLOUD upsampling method.
Definition mls.h:375
void setDilationVoxelSize(float voxel_size)
Set the voxel size for the voxel grid.
Definition mls.h:429
UpsamplingMethod upsample_method_
Parameter that specifies the upsampling method to be used.
Definition mls.h:529
int searchForNeighbors(pcl::index_t index, pcl::Indices &indices, std::vector< float > &sqr_distances) const
Search for the nearest neighbors of a given point using a radius search.
Definition mls.h:639
double upsampling_radius_
Radius of the circle in the local point plane that will be sampled.
Definition mls.h:534
@ RANDOM_UNIFORM_DENSITY
The local plane of each input point will be sampled using an uniform random distribution such that th...
Definition mls.h:286
@ SAMPLE_LOCAL_PLANE
The local plane of each input point will be sampled in a circular fashion using the upsampling_radius...
Definition mls.h:284
@ VOXEL_GRID_DILATION
The input cloud will be inserted into a voxel grid with voxels of size voxel_size_; this voxel grid w...
Definition mls.h:289
@ NONE
No upsampling will be done, only the input points will be projected to their own MLS surfaces.
Definition mls.h:281
@ DISTINCT_CLOUD
Project the points of the distinct cloud to the MLS surface.
Definition mls.h:283
PointCloudInConstPtr getDistinctCloud() const
Get the distinct cloud used for the DISTINCT_CLOUD upsampling method.
Definition mls.h:379
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
Definition mls.h:320
int desired_num_points_in_radius_
Parameter that specifies the desired number of points within the search radius.
Definition mls.h:544
pcl::PointCloud< pcl::Normal > NormalCloud
Definition mls.h:266
void setUpsamplingMethod(UpsamplingMethod method)
Set the upsampling method to be used.
Definition mls.h:371
void setUpsamplingStepSize(double step_size)
Set the step size for the local plane sampling.
Definition mls.h:400
PointIndicesPtr corresponding_input_indices_
Collects for each point in output the corresponding point in the input.
Definition mls.h:631
void setCacheMLSResults(bool cache_mls_results)
Set whether the mls results should be stored for each point in the input cloud.
Definition mls.h:457
int nr_coeff_
Number of coefficients, to be computed from the requested order.
Definition mls.h:628
void setNumberOfThreads(unsigned int threads=1)
Set the maximum number of threads to use.
Definition mls.h:486
bool compute_normals_
Parameter that specifies whether the normals should be computed for the input cloud or not.
Definition mls.h:526
void performProcessing(PointCloudOut &output) override
Abstract surface reconstruction method.
Definition mls.hpp:286
void computeMLSPointNormal(pcl::index_t index, const pcl::Indices &nn_indices, PointCloudOut &projected_points, NormalCloud &projected_points_normals, PointIndices &corresponding_input_indices, MLSResult &mls_result) const
Smooth a given point and its neighborghood using Moving Least Squares.
Definition mls.hpp:176
shared_ptr< const MovingLeastSquares< PointInT, PointOutT > > ConstPtr
Definition mls.h:256
const std::vector< MLSResult > & getMLSResults() const
Get the MLSResults for input cloud.
Definition mls.h:480
SearchMethod search_method_
The search method template for indices.
Definition mls.h:511
void process(PointCloudOut &output) override
Base method for surface reconstruction for all points given in <setInputCloud (), setIndices ()>
Definition mls.hpp:63
void addProjectedPointNormal(pcl::index_t index, const Eigen::Vector3d &point, const Eigen::Vector3d &normal, double curvature, PointCloudOut &projected_points, NormalCloud &projected_points_normals, PointIndices &corresponding_input_indices) const
This is a helper function for adding projected points.
Definition mls.hpp:254
PointIndicesPtr getCorrespondingIndices() const
Get the set of indices with each point in output having the corresponding point in input.
Definition mls.h:501
void setSearchRadius(double radius)
Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting.
Definition mls.h:350
typename PointCloudOut::ConstPtr PointCloudOutConstPtr
Definition mls.h:271
int dilation_iteration_num_
Number of dilation steps for the VOXEL_GRID_DILATION upsampling method.
Definition mls.h:625
void setProjectionMethod(MLSResult::ProjectionMethod method)
Set the method to be used when projection the point on to the MLS surface.
Definition mls.h:468
bool cache_mls_results_
True if the mls results for the input cloud should be stored.
Definition mls.h:549
~MovingLeastSquares() override=default
Empty destructor.
std::vector< MLSResult > mls_results_
Stores the MLS result for each point in the input cloud.
Definition mls.h:554
float voxel_size_
Voxel size for the VOXEL_GRID_DILATION upsampling method.
Definition mls.h:622
PointCloudInConstPtr distinct_cloud_
The distinct point cloud that will be projected to the MLS surface.
Definition mls.h:508
PCL base class.
Definition pcl_base.h:70
PointCloudConstPtr input_
The input point cloud dataset.
Definition pcl_base.h:147
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition pcl_base.h:150
bool initCompute()
This method should get called before starting the actual computation.
Definition pcl_base.hpp:138
bool fake_indices_
If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud.
Definition pcl_base.h:156
bool deinitCompute()
This method should get called after finishing the actual computation.
Definition pcl_base.hpp:175
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< PointCloud< pcl::Normal > > Ptr
shared_ptr< const PointCloud< PointOutT > > ConstPtr
shared_ptr< pcl::search::Search< PointInT > > Ptr
Definition search.h:81
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition memory.h:86
Defines functions, macros and traits for allocating and using memory.
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition types.h:112
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
PointIndices::Ptr PointIndicesPtr
shared_ptr< Indices > IndicesPtr
Definition pcl_base.h:58
Defines all the PCL and non-PCL macros used.
Data structure used to store the MLS projection results.
Definition mls.h:81
Eigen::Vector3d point
The projected point.
Definition mls.h:86
double v
The v-coordinate of the projected point in local MLS frame.
Definition mls.h:85
Eigen::Vector3d normal
The projected point's normal.
Definition mls.h:87
double u
The u-coordinate of the projected point in local MLS frame.
Definition mls.h:84
Data structure used to store the MLS polynomial partial derivatives.
Definition mls.h:70
double z_uv
The partial derivative d^2z/dudv.
Definition mls.h:76
double z_u
The partial derivative dz/du.
Definition mls.h:72
double z_uu
The partial derivative d^2z/du^2.
Definition mls.h:74
double z
The z component of the polynomial evaluated at z(u, v).
Definition mls.h:71
double z_vv
The partial derivative d^2z/dv^2.
Definition mls.h:75
double z_v
The partial derivative dz/dv.
Definition mls.h:73
Data structure used to store the results of the MLS fitting.
Definition mls.h:60
MLSProjectionResults projectPoint(const Eigen::Vector3d &pt, ProjectionMethod method, int required_neighbors=0) const
Project a point using the specified method.
Definition mls.hpp:639
MLSResult()
Definition mls.h:92
Eigen::Vector3d mean
The mean point of all the neighbors.
Definition mls.h:216
MLSProjectionResults projectPointOrthogonalToPolynomialSurface(const double u, const double v, const double w) const
Project a point orthogonal to the polynomial surface.
Definition mls.hpp:539
Eigen::Vector3d u_axis
The axis corresponding to the u-coordinates of the local plane of the query point.
Definition mls.h:218
Eigen::Vector3d plane_normal
The normal of the local plane of the query point.
Definition mls.h:217
ProjectionMethod
Definition mls.h:62
@ ORTHOGONAL
Project to the closest point on the polynonomial surface.
Definition mls.h:65
@ SIMPLE
Project along the mls plane normal to the polynomial surface.
Definition mls.h:64
@ NONE
Project to the mls plane.
Definition mls.h:63
Eigen::Vector3d v_axis
The axis corresponding to the v-coordinates of the local plane of the query point.
Definition mls.h:219
int num_neighbors
The number of neighbors used to create the mls surface.
Definition mls.h:221
Eigen::VectorXd c_vec
The polynomial coefficients Example: z = c_vec[0] + c_vec[1]*v + c_vec[2]*v^2 + c_vec[3]*u + c_vec[4]...
Definition mls.h:220
void computeMLSSurface(const pcl::PointCloud< PointT > &cloud, pcl::index_t index, const pcl::Indices &nn_indices, double search_radius, int polynomial_order=2, std::function< double(const double)> weight_func={})
Smooth a given point and its neighborhood using Moving Least Squares.
Definition mls.hpp:692
void getMLSCoordinates(const Eigen::Vector3d &pt, double &u, double &v, double &w) const
Given a point calculate its 3D location in the MLS frame.
Definition mls.hpp:455
float curvature
The curvature at the query point.
Definition mls.h:222
PolynomialPartialDerivative getPolynomialPartialDerivative(const double u, const double v) const
Calculate the polynomial's first and second partial derivatives.
Definition mls.hpp:494
MLSProjectionResults projectPointSimpleToPolynomialSurface(const double u, const double v) const
Project a point along the MLS plane normal to the polynomial surface.
Definition mls.hpp:616
MLSProjectionResults projectPointToMLSPlane(const double u, const double v) const
Project a point onto the MLS plane.
Definition mls.hpp:604
Eigen::Vector2f calculatePrincipalCurvatures(const double u, const double v) const
Calculate the principal curvatures using the polynomial surface.
double getPolynomialValue(const double u, const double v) const
Calculate the polynomial.
Definition mls.hpp:472
Eigen::Vector3d query_point
The query point about which the mls surface was generated.
Definition mls.h:215
MLSProjectionResults projectQueryPoint(ProjectionMethod method, int required_neighbors=0) const
Project the query point used to generate the mls surface about using the specified method.
Definition mls.hpp:661
int order
The order of the polynomial.
Definition mls.h:223
bool valid
If True, the mls results data is valid, otherwise False.
Definition mls.h:224