Point Cloud Library (PCL)  1.11.0-dev
normal_3d.h
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40 
41 #pragma once
42 
43 #include <pcl/memory.h>
44 #include <pcl/pcl_macros.h>
45 #include <pcl/features/feature.h>
46 #include <pcl/common/centroid.h>
47 
48 namespace pcl
49 {
50  /** \brief Compute the Least-Squares plane fit for a given set of points, and return the estimated plane
51  * parameters together with the surface curvature.
52  * \param cloud the input point cloud
53  * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
54  * \param curvature the estimated surface curvature as a measure of
55  * \f[
56  * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
57  * \f]
58  * \ingroup features
59  */
60  template <typename PointT> inline bool
62  Eigen::Vector4f &plane_parameters, float &curvature)
63  {
64  // Placeholder for the 3x3 covariance matrix at each surface patch
65  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
66  // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
67  Eigen::Vector4f xyz_centroid;
68 
69  if (cloud.size () < 3 ||
70  computeMeanAndCovarianceMatrix (cloud, covariance_matrix, xyz_centroid) == 0)
71  {
72  plane_parameters.setConstant (std::numeric_limits<float>::quiet_NaN ());
73  curvature = std::numeric_limits<float>::quiet_NaN ();
74  return false;
75  }
76 
77  // Get the plane normal and surface curvature
78  solvePlaneParameters (covariance_matrix, xyz_centroid, plane_parameters, curvature);
79  return true;
80  }
81 
82  /** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
83  * and return the estimated plane parameters together with the surface curvature.
84  * \param cloud the input point cloud
85  * \param indices the point cloud indices that need to be used
86  * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
87  * \param curvature the estimated surface curvature as a measure of
88  * \f[
89  * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
90  * \f]
91  * \ingroup features
92  */
93  template <typename PointT> inline bool
94  computePointNormal (const pcl::PointCloud<PointT> &cloud, const std::vector<int> &indices,
95  Eigen::Vector4f &plane_parameters, float &curvature)
96  {
97  // Placeholder for the 3x3 covariance matrix at each surface patch
98  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
99  // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
100  Eigen::Vector4f xyz_centroid;
101  if (indices.size () < 3 ||
102  computeMeanAndCovarianceMatrix (cloud, indices, covariance_matrix, xyz_centroid) == 0)
103  {
104  plane_parameters.setConstant (std::numeric_limits<float>::quiet_NaN ());
105  curvature = std::numeric_limits<float>::quiet_NaN ();
106  return false;
107  }
108  // Get the plane normal and surface curvature
109  solvePlaneParameters (covariance_matrix, xyz_centroid, plane_parameters, curvature);
110  return true;
111  }
112 
113  /** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
114  * \param point a given point
115  * \param vp_x the X coordinate of the viewpoint
116  * \param vp_y the X coordinate of the viewpoint
117  * \param vp_z the X coordinate of the viewpoint
118  * \param normal the plane normal to be flipped
119  * \ingroup features
120  */
121  template <typename PointT, typename Scalar> inline void
122  flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z,
123  Eigen::Matrix<Scalar, 4, 1>& normal)
124  {
125  Eigen::Matrix <Scalar, 4, 1> vp (vp_x - point.x, vp_y - point.y, vp_z - point.z, 0);
126 
127  // Dot product between the (viewpoint - point) and the plane normal
128  float cos_theta = vp.dot (normal);
129 
130  // Flip the plane normal
131  if (cos_theta < 0)
132  {
133  normal *= -1;
134  normal[3] = 0.0f;
135  // Hessian form (D = nc . p_plane (centroid here) + p)
136  normal[3] = -1 * normal.dot (point.getVector4fMap ());
137  }
138  }
139 
140  /** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
141  * \param point a given point
142  * \param vp_x the X coordinate of the viewpoint
143  * \param vp_y the X coordinate of the viewpoint
144  * \param vp_z the X coordinate of the viewpoint
145  * \param normal the plane normal to be flipped
146  * \ingroup features
147  */
148  template <typename PointT, typename Scalar> inline void
149  flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z,
150  Eigen::Matrix<Scalar, 3, 1>& normal)
151  {
152  Eigen::Matrix <Scalar, 3, 1> vp (vp_x - point.x, vp_y - point.y, vp_z - point.z);
153 
154  // Flip the plane normal
155  if (vp.dot (normal) < 0)
156  normal *= -1;
157  }
158 
159  /** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
160  * \param point a given point
161  * \param vp_x the X coordinate of the viewpoint
162  * \param vp_y the X coordinate of the viewpoint
163  * \param vp_z the X coordinate of the viewpoint
164  * \param nx the resultant X component of the plane normal
165  * \param ny the resultant Y component of the plane normal
166  * \param nz the resultant Z component of the plane normal
167  * \ingroup features
168  */
169  template <typename PointT> inline void
170  flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z,
171  float &nx, float &ny, float &nz)
172  {
173  // See if we need to flip any plane normals
174  vp_x -= point.x;
175  vp_y -= point.y;
176  vp_z -= point.z;
177 
178  // Dot product between the (viewpoint - point) and the plane normal
179  float cos_theta = (vp_x * nx + vp_y * ny + vp_z * nz);
180 
181  // Flip the plane normal
182  if (cos_theta < 0)
183  {
184  nx *= -1;
185  ny *= -1;
186  nz *= -1;
187  }
188  }
189 
190  /** \brief Flip (in place) normal to get the same sign of the mean of the normals specified by normal_indices.
191  *
192  * The method is described in:
193  * A. Petrelli, L. Di Stefano, "A repeatable and efficient canonical reference for surface matching", 3DimPVT, 2012
194  * A. Petrelli, L. Di Stefano, "On the repeatability of the local reference frame for partial shape matching", 13th International Conference on Computer Vision (ICCV), 2011
195  *
196  * Normals should be unit vectors. Otherwise the resulting mean would be weighted by the normal norms.
197  * \param[in] normal_cloud Cloud of normals used to compute the mean
198  * \param[in] normal_indices Indices of normals used to compute the mean
199  * \param[in] normal input Normal to flip. Normal is modified by the function.
200  * \return false if normal_indices does not contain any valid normal.
201  * \ingroup features
202  */
203  template<typename PointNT> inline bool
205  std::vector<int> const &normal_indices,
206  Eigen::Vector3f &normal)
207  {
208  Eigen::Vector3f normal_mean = Eigen::Vector3f::Zero ();
209 
210  for (const int &normal_index : normal_indices)
211  {
212  const PointNT& cur_pt = normal_cloud[normal_index];
213 
214  if (pcl::isFinite (cur_pt))
215  {
216  normal_mean += cur_pt.getNormalVector3fMap ();
217  }
218  }
219 
220  if (normal_mean.isZero ())
221  return false;
222 
223  normal_mean.normalize ();
224 
225  if (normal.dot (normal_mean) < 0)
226  {
227  normal = -normal;
228  }
229 
230  return true;
231  }
232 
233  /** \brief NormalEstimation estimates local surface properties (surface normals and curvatures)at each
234  * 3D point. If PointOutT is specified as pcl::Normal, the normal is stored in the first 3 components (0-2),
235  * and the curvature is stored in component 3.
236  *
237  * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
238  * \ref NormalEstimationOMP for a parallel implementation.
239  * \author Radu B. Rusu
240  * \ingroup features
241  */
242  template <typename PointInT, typename PointOutT>
243  class NormalEstimation: public Feature<PointInT, PointOutT>
244  {
245  public:
246  using Ptr = shared_ptr<NormalEstimation<PointInT, PointOutT> >;
247  using ConstPtr = shared_ptr<const NormalEstimation<PointInT, PointOutT> >;
256 
259 
260  /** \brief Empty constructor. */
262  : vpx_ (0)
263  , vpy_ (0)
264  , vpz_ (0)
265  , use_sensor_origin_ (true)
266  {
267  feature_name_ = "NormalEstimation";
268  };
269 
270  /** \brief Empty destructor */
272 
273  /** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
274  * and return the estimated plane parameters together with the surface curvature.
275  * \param cloud the input point cloud
276  * \param indices the point cloud indices that need to be used
277  * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
278  * \param curvature the estimated surface curvature as a measure of
279  * \f[
280  * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
281  * \f]
282  */
283  inline bool
284  computePointNormal (const pcl::PointCloud<PointInT> &cloud, const std::vector<int> &indices,
285  Eigen::Vector4f &plane_parameters, float &curvature)
286  {
287  if (indices.size () < 3 ||
289  {
290  plane_parameters.setConstant (std::numeric_limits<float>::quiet_NaN ());
291  curvature = std::numeric_limits<float>::quiet_NaN ();
292  return false;
293  }
294 
295  // Get the plane normal and surface curvature
296  solvePlaneParameters (covariance_matrix_, xyz_centroid_, plane_parameters, curvature);
297  return true;
298  }
299 
300  /** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
301  * and return the estimated plane parameters together with the surface curvature.
302  * \param cloud the input point cloud
303  * \param indices the point cloud indices that need to be used
304  * \param nx the resultant X component of the plane normal
305  * \param ny the resultant Y component of the plane normal
306  * \param nz the resultant Z component of the plane normal
307  * \param curvature the estimated surface curvature as a measure of
308  * \f[
309  * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
310  * \f]
311  */
312  inline bool
313  computePointNormal (const pcl::PointCloud<PointInT> &cloud, const std::vector<int> &indices,
314  float &nx, float &ny, float &nz, float &curvature)
315  {
316  if (indices.size () < 3 ||
318  {
319  nx = ny = nz = curvature = std::numeric_limits<float>::quiet_NaN ();
320  return false;
321  }
322 
323  // Get the plane normal and surface curvature
324  solvePlaneParameters (covariance_matrix_, nx, ny, nz, curvature);
325  return true;
326  }
327 
328  /** \brief Provide a pointer to the input dataset
329  * \param cloud the const boost shared pointer to a PointCloud message
330  */
331  inline void
332  setInputCloud (const PointCloudConstPtr &cloud) override
333  {
334  input_ = cloud;
335  if (use_sensor_origin_)
336  {
337  vpx_ = input_->sensor_origin_.coeff (0);
338  vpy_ = input_->sensor_origin_.coeff (1);
339  vpz_ = input_->sensor_origin_.coeff (2);
340  }
341  }
342 
343  /** \brief Set the viewpoint.
344  * \param vpx the X coordinate of the viewpoint
345  * \param vpy the Y coordinate of the viewpoint
346  * \param vpz the Z coordinate of the viewpoint
347  */
348  inline void
349  setViewPoint (float vpx, float vpy, float vpz)
350  {
351  vpx_ = vpx;
352  vpy_ = vpy;
353  vpz_ = vpz;
354  use_sensor_origin_ = false;
355  }
356 
357  /** \brief Get the viewpoint.
358  * \param [out] vpx x-coordinate of the view point
359  * \param [out] vpy y-coordinate of the view point
360  * \param [out] vpz z-coordinate of the view point
361  * \note this method returns the currently used viewpoint for normal flipping.
362  * If the viewpoint is set manually using the setViewPoint method, this method will return the set view point coordinates.
363  * If an input cloud is set, it will return the sensor origin otherwise it will return the origin (0, 0, 0)
364  */
365  inline void
366  getViewPoint (float &vpx, float &vpy, float &vpz)
367  {
368  vpx = vpx_;
369  vpy = vpy_;
370  vpz = vpz_;
371  }
372 
373  /** \brief sets whether the sensor origin or a user given viewpoint should be used. After this method, the
374  * normal estimation method uses the sensor origin of the input cloud.
375  * to use a user defined view point, use the method setViewPoint
376  */
377  inline void
379  {
380  use_sensor_origin_ = true;
381  if (input_)
382  {
383  vpx_ = input_->sensor_origin_.coeff (0);
384  vpy_ = input_->sensor_origin_.coeff (1);
385  vpz_ = input_->sensor_origin_.coeff (2);
386  }
387  else
388  {
389  vpx_ = 0;
390  vpy_ = 0;
391  vpz_ = 0;
392  }
393  }
394 
395  protected:
396  /** \brief Estimate normals for all points given in <setInputCloud (), setIndices ()> using the surface in
397  * setSearchSurface () and the spatial locator in setSearchMethod ()
398  * \note In situations where not enough neighbors are found, the normal and curvature values are set to NaN.
399  * \param output the resultant point cloud model dataset that contains surface normals and curvatures
400  */
401  void
402  computeFeature (PointCloudOut &output) override;
403 
404  /** \brief Values describing the viewpoint ("pinhole" camera model assumed). For per point viewpoints, inherit
405  * from NormalEstimation and provide your own computeFeature (). By default, the viewpoint is set to 0,0,0. */
406  float vpx_, vpy_, vpz_;
407 
408  /** \brief Placeholder for the 3x3 covariance matrix at each surface patch. */
409  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_;
410 
411  /** \brief 16-bytes aligned placeholder for the XYZ centroid of a surface patch. */
412  Eigen::Vector4f xyz_centroid_;
413 
414  /** whether the sensor origin of the input cloud or a user given viewpoint should be used.*/
416 
417  public:
419  };
420 }
421 
422 #ifdef PCL_NO_PRECOMPILE
423 #include <pcl/features/impl/normal_3d.hpp>
424 #endif
pcl::computeMeanAndCovarianceMatrix
unsigned int computeMeanAndCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single lo...
Definition: centroid.hpp:485
pcl_macros.h
Defines all the PCL and non-PCL macros used.
pcl::NormalEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate normals for all points given in <setInputCloud (), setIndices ()> using the surface in setSe...
Definition: normal_3d.hpp:48
pcl
Definition: convolution.h:46
pcl::Feature< PointInT, PointNT >::Ptr
shared_ptr< Feature< PointInT, PointNT > > Ptr
Definition: feature.h:114
pcl::NormalEstimation::NormalEstimation
NormalEstimation()
Empty constructor.
Definition: normal_3d.h:261
pcl::NormalEstimation
NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point.
Definition: normal_3d.h:243
pcl::PCLBase< PointInT >::PointCloudConstPtr
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: pcl_base.h:74
pcl::PCLBase< PointInT >::input_
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:147
pcl::isFinite
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
pcl::NormalEstimation::computePointNormal
bool computePointNormal(const pcl::PointCloud< PointInT > &cloud, const std::vector< int > &indices, Eigen::Vector4f &plane_parameters, float &curvature)
Compute the Least-Squares plane fit for a given set of points, using their indices,...
Definition: normal_3d.h:284
pcl::solvePlaneParameters
void solvePlaneParameters(const Eigen::Matrix3f &covariance_matrix, const Eigen::Vector4f &point, Eigen::Vector4f &plane_parameters, float &curvature)
Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squar...
Definition: feature.hpp:51
pcl::NormalEstimation::computePointNormal
bool computePointNormal(const pcl::PointCloud< PointInT > &cloud, const std::vector< int > &indices, float &nx, float &ny, float &nz, float &curvature)
Compute the Least-Squares plane fit for a given set of points, using their indices,...
Definition: normal_3d.h:313
pcl::NormalEstimation::getViewPoint
void getViewPoint(float &vpx, float &vpy, float &vpz)
Get the viewpoint.
Definition: normal_3d.h:366
pcl::NormalEstimation::vpy_
float vpy_
Definition: normal_3d.h:406
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: projection_matrix.h:53
pcl::Feature< PointInT, PointNT >::ConstPtr
shared_ptr< const Feature< PointInT, PointNT > > ConstPtr
Definition: feature.h:115
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:628
pcl::computePointNormal
bool computePointNormal(const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &plane_parameters, float &curvature)
Compute the Least-Squares plane fit for a given set of points, and return the estimated plane paramet...
Definition: normal_3d.h:61
pcl::NormalEstimation::setInputCloud
void setInputCloud(const PointCloudConstPtr &cloud) override
Provide a pointer to the input dataset.
Definition: normal_3d.h:332
pcl::flipNormalTowardsViewpoint
void flipNormalTowardsViewpoint(const PointT &point, float vp_x, float vp_y, float vp_z, Eigen::Matrix< Scalar, 4, 1 > &normal)
Flip (in place) the estimated normal of a point towards a given viewpoint.
Definition: normal_3d.h:122
pcl::NormalEstimation::vpz_
float vpz_
Definition: normal_3d.h:406
PCL_MAKE_ALIGNED_OPERATOR_NEW
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
pcl::NormalEstimation::vpx_
float vpx_
Values describing the viewpoint ("pinhole" camera model assumed).
Definition: normal_3d.h:406
pcl::NormalEstimation::use_sensor_origin_
bool use_sensor_origin_
whether the sensor origin of the input cloud or a user given viewpoint should be used.
Definition: normal_3d.h:415
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:459
pcl::NormalEstimation::~NormalEstimation
~NormalEstimation()
Empty destructor.
Definition: normal_3d.h:271
pcl::NormalEstimation::setViewPoint
void setViewPoint(float vpx, float vpy, float vpz)
Set the viewpoint.
Definition: normal_3d.h:349
pcl::NormalEstimation::covariance_matrix_
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_
Placeholder for the 3x3 covariance matrix at each surface patch.
Definition: normal_3d.h:409
pcl::NormalEstimation::useSensorOriginAsViewPoint
void useSensorOriginAsViewPoint()
sets whether the sensor origin or a user given viewpoint should be used.
Definition: normal_3d.h:378
pcl::flipNormalTowardsNormalsMean
bool flipNormalTowardsNormalsMean(pcl::PointCloud< PointNT > const &normal_cloud, std::vector< int > const &normal_indices, Eigen::Vector3f &normal)
Flip (in place) normal to get the same sign of the mean of the normals specified by normal_indices.
Definition: normal_3d.h:204
pcl::Feature::feature_name_
std::string feature_name_
The feature name.
Definition: feature.h:223
pcl::NormalEstimation::PointCloudOut
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: normal_3d.h:257
memory.h
Defines functions, macros and traits for allocating and using memory.
centroid.h
pcl::NormalEstimation::xyz_centroid_
Eigen::Vector4f xyz_centroid_
16-bytes aligned placeholder for the XYZ centroid of a surface patch.
Definition: normal_3d.h:412
pcl::Feature
Feature represents the base feature class.
Definition: feature.h:106