Point Cloud Library (PCL) 1.12.1
sac_model_normal_plane.h
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40
41#pragma once
42
43#include <pcl/memory.h>
44#include <pcl/pcl_macros.h>
45#include <pcl/sample_consensus/sac_model.h>
46#include <pcl/sample_consensus/sac_model_plane.h>
47#include <pcl/sample_consensus/model_types.h>
48
49namespace pcl
50{
51 /** \brief SampleConsensusModelNormalPlane defines a model for 3D plane
52 * segmentation using additional surface normal constraints. Basically this
53 * means that checking for inliers will not only involve a "distance to
54 * model" criterion, but also an additional "maximum angular deviation"
55 * between the plane's normal and the inlier points normals.
56 *
57 * The model coefficients are defined as:
58 * - \b a : the X coordinate of the plane's normal (normalized)
59 * - \b b : the Y coordinate of the plane's normal (normalized)
60 * - \b c : the Z coordinate of the plane's normal (normalized)
61 * - \b d : the fourth <a href="http://mathworld.wolfram.com/HessianNormalForm.html">Hessian component</a> of the plane's equation
62 *
63 * To set the influence of the surface normals in the inlier estimation
64 * process, set the normal weight (0.0-1.0), e.g.:
65 * \code
66 * SampleConsensusModelNormalPlane<pcl::PointXYZ, pcl::Normal> sac_model;
67 * ...
68 * sac_model.setNormalDistanceWeight (0.1);
69 * ...
70 * \endcode
71 *
72 * \author Radu B. Rusu and Jared Glover
73 * \ingroup sample_consensus
74 */
75 template <typename PointT, typename PointNT>
77 {
78 public:
86
90
93
94 using Ptr = shared_ptr<SampleConsensusModelNormalPlane<PointT, PointNT> >;
95 using ConstPtr = shared_ptr<const SampleConsensusModelNormalPlane<PointT, PointNT>>;
96
97 /** \brief Constructor for base SampleConsensusModelNormalPlane.
98 * \param[in] cloud the input point cloud dataset
99 * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
100 */
102 bool random = false)
103 : SampleConsensusModelPlane<PointT> (cloud, random)
105 {
106 model_name_ = "SampleConsensusModelNormalPlane";
107 sample_size_ = 3;
108 model_size_ = 4;
109 }
110
111 /** \brief Constructor for base SampleConsensusModelNormalPlane.
112 * \param[in] cloud the input point cloud dataset
113 * \param[in] indices a vector of point indices to be used from \a cloud
114 * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
115 */
117 const Indices &indices,
118 bool random = false)
119 : SampleConsensusModelPlane<PointT> (cloud, indices, random)
121 {
122 model_name_ = "SampleConsensusModelNormalPlane";
123 sample_size_ = 3;
124 model_size_ = 4;
125 }
126
127 /** \brief Empty destructor */
129
130 /** \brief Select all the points which respect the given model coefficients as inliers.
131 * \param[in] model_coefficients the coefficients of a plane model that we need to compute distances to
132 * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
133 * \param[out] inliers the resultant model inliers
134 */
135 void
136 selectWithinDistance (const Eigen::VectorXf &model_coefficients,
137 const double threshold,
138 Indices &inliers) override;
139
140 /** \brief Count all the points which respect the given model coefficients as inliers.
141 *
142 * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
143 * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
144 * \return the resultant number of inliers
145 */
146 std::size_t
147 countWithinDistance (const Eigen::VectorXf &model_coefficients,
148 const double threshold) const override;
149
150 /** \brief Compute all distances from the cloud data to a given plane model.
151 * \param[in] model_coefficients the coefficients of a plane model that we need to compute distances to
152 * \param[out] distances the resultant estimated distances
153 */
154 void
155 getDistancesToModel (const Eigen::VectorXf &model_coefficients,
156 std::vector<double> &distances) const override;
157
158 /** \brief Return a unique id for this model (SACMODEL_NORMAL_PLANE). */
159 inline pcl::SacModel
160 getModelType () const override { return (SACMODEL_NORMAL_PLANE); }
161
163
164 protected:
167
168 /** This implementation uses no SIMD instructions. It is not intended for normal use.
169 * See countWithinDistance which automatically uses the fastest implementation.
170 */
171 std::size_t
172 countWithinDistanceStandard (const Eigen::VectorXf &model_coefficients,
173 const double threshold,
174 std::size_t i = 0) const;
175
176#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
177 /** This implementation uses SSE, SSE2, and SSE4.1 instructions. It is not intended for normal use.
178 * See countWithinDistance which automatically uses the fastest implementation.
179 */
180 std::size_t
181 countWithinDistanceSSE (const Eigen::VectorXf &model_coefficients,
182 const double threshold,
183 std::size_t i = 0) const;
184#endif
185
186#if defined (__AVX__) && defined (__AVX2__)
187 /** This implementation uses AVX and AVX2 instructions. It is not intended for normal use.
188 * See countWithinDistance which automatically uses the fastest implementation.
189 */
190 std::size_t
191 countWithinDistanceAVX (const Eigen::VectorXf &model_coefficients,
192 const double threshold,
193 std::size_t i = 0) const;
194#endif
195 };
196}
197
198#ifdef PCL_NO_PRECOMPILE
199#include <pcl/sample_consensus/impl/sac_model_normal_plane.hpp>
200#endif
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
SampleConsensusModelFromNormals represents the base model class for models that require the use of su...
Definition: sac_model.h:612
typename pcl::PointCloud< PointNT >::ConstPtr PointCloudNConstPtr
Definition: sac_model.h:614
typename pcl::PointCloud< PointNT >::Ptr PointCloudNPtr
Definition: sac_model.h:615
SampleConsensusModel represents the base model class.
Definition: sac_model.h:70
shared_ptr< SampleConsensusModel< PointT > > Ptr
Definition: sac_model.h:77
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:588
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: sac_model.h:73
std::string model_name_
The model name.
Definition: sac_model.h:550
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:591
typename PointCloud::Ptr PointCloudPtr
Definition: sac_model.h:74
shared_ptr< const SampleConsensusModel< PointT > > ConstPtr
Definition: sac_model.h:78
SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface no...
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.
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.
SampleConsensusModelNormalPlane(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelNormalPlane.
SampleConsensusModelNormalPlane(const PointCloudConstPtr &cloud, const Indices &indices, bool random=false)
Constructor for base SampleConsensusModelNormalPlane.
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all distances from the cloud data to a given plane model.
std::size_t countWithinDistanceStandard(const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i=0) const
This implementation uses no SIMD instructions.
pcl::SacModel getModelType() const override
Return a unique id for this model (SACMODEL_NORMAL_PLANE).
SampleConsensusModelPlane defines a model for 3D plane segmentation.
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
Defines functions, macros and traits for allocating and using memory.
SacModel
Definition: model_types.h:46
@ SACMODEL_NORMAL_PLANE
Definition: model_types.h:58
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
Defines all the PCL and non-PCL macros used.
A point structure representing Euclidean xyz coordinates, and the RGB color.