sac_model_normal_plane.h
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#pragma once
#include <pcl/memory.h>
#include <pcl/pcl_macros.h>
#include <pcl/sample_consensus/sac_model.h>
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/sample_consensus/model_types.h>
namespace pcl
{
/** \brief SampleConsensusModelNormalPlane defines a model for 3D plane
* segmentation using additional surface normal constraints. Basically this
* means that checking for inliers will not only involve a "distance to
* model" criterion, but also an additional "maximum angular deviation"
* between the plane's normal and the inlier points normals.
*
* The model coefficients are defined as:
* - \b a : the X coordinate of the plane's normal (normalized)
* - \b b : the Y coordinate of the plane's normal (normalized)
* - \b c : the Z coordinate of the plane's normal (normalized)
* - \b d : the fourth <a href="http://mathworld.wolfram.com/HessianNormalForm.html">Hessian component</a> of the plane's equation
*
* To set the influence of the surface normals in the inlier estimation
* process, set the normal weight (0.0-1.0), e.g.:
* \code
* SampleConsensusModelNormalPlane<pcl::PointXYZ, pcl::Normal> sac_model;
* ...
* sac_model.setNormalDistanceWeight (0.1);
* ...
* \endcode
*
* \author Radu B. Rusu and Jared Glover
* \ingroup sample_consensus
*/
template <typename PointT, typename PointNT>
class SampleConsensusModelNormalPlane : public SampleConsensusModelPlane<PointT>, public SampleConsensusModelFromNormals<PointT, PointNT>
{
public:
using SampleConsensusModel<PointT>::model_name_;
using SampleConsensusModel<PointT>::input_;
using SampleConsensusModel<PointT>::indices_;
using SampleConsensusModelFromNormals<PointT, PointNT>::normals_;
using SampleConsensusModelFromNormals<PointT, PointNT>::normal_distance_weight_;
using SampleConsensusModel<PointT>::error_sqr_dists_;
using SampleConsensusModel<PointT>::isModelValid;
using PointCloud = typename SampleConsensusModel<PointT>::PointCloud;
using PointCloudPtr = typename SampleConsensusModel<PointT>::PointCloudPtr;
using PointCloudConstPtr = typename SampleConsensusModel<PointT>::PointCloudConstPtr;
using PointCloudNPtr = typename SampleConsensusModelFromNormals<PointT, PointNT>::PointCloudNPtr;
using PointCloudNConstPtr = typename SampleConsensusModelFromNormals<PointT, PointNT>::PointCloudNConstPtr;
using Ptr = shared_ptr<SampleConsensusModelNormalPlane<PointT, PointNT> >;
using ConstPtr = shared_ptr<const SampleConsensusModelNormalPlane<PointT, PointNT>>;
/** \brief Constructor for base SampleConsensusModelNormalPlane.
* \param[in] cloud the input point cloud dataset
* \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
*/
SampleConsensusModelNormalPlane (const PointCloudConstPtr &cloud,
bool random = false)
: SampleConsensusModelPlane<PointT> (cloud, random)
, SampleConsensusModelFromNormals<PointT, PointNT> ()
{
model_name_ = "SampleConsensusModelNormalPlane";
sample_size_ = 3;
model_size_ = 4;
}
/** \brief Constructor for base SampleConsensusModelNormalPlane.
* \param[in] cloud the input point cloud dataset
* \param[in] indices a vector of point indices to be used from \a cloud
* \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
*/
SampleConsensusModelNormalPlane (const PointCloudConstPtr &cloud,
const Indices &indices,
bool random = false)
: SampleConsensusModelPlane<PointT> (cloud, indices, random)
, SampleConsensusModelFromNormals<PointT, PointNT> ()
{
model_name_ = "SampleConsensusModelNormalPlane";
sample_size_ = 3;
model_size_ = 4;
}
/** \brief Empty destructor */
~SampleConsensusModelNormalPlane () {}
/** \brief Select all the points which respect the given model coefficients as inliers.
* \param[in] model_coefficients the coefficients of a plane model that we need to compute distances to
* \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
* \param[out] inliers the resultant model inliers
*/
void
selectWithinDistance (const Eigen::VectorXf &model_coefficients,
const double threshold,
Indices &inliers) override;
/** \brief Count all the points which respect the given model coefficients as inliers.
*
* \param[in] model_coefficients the coefficients of a model that we need to compute distances to
* \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
* \return the resultant number of inliers
*/
std::size_t
countWithinDistance (const Eigen::VectorXf &model_coefficients,
const double threshold) const override;
/** \brief Compute all distances from the cloud data to a given plane model.
* \param[in] model_coefficients the coefficients of a plane model that we need to compute distances to
* \param[out] distances the resultant estimated distances
*/
void
getDistancesToModel (const Eigen::VectorXf &model_coefficients,
std::vector<double> &distances) const override;
/** \brief Return a unique id for this model (SACMODEL_NORMAL_PLANE). */
inline pcl::SacModel
getModelType () const override { return (SACMODEL_NORMAL_PLANE); }
PCL_MAKE_ALIGNED_OPERATOR_NEW
protected:
using SampleConsensusModel<PointT>::sample_size_;
using SampleConsensusModel<PointT>::model_size_;
/** This implementation uses no SIMD instructions. It is not intended for normal use.
* See countWithinDistance which automatically uses the fastest implementation.
*/
std::size_t
countWithinDistanceStandard (const Eigen::VectorXf &model_coefficients,
const double threshold,
std::size_t i = 0) const;
#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
/** This implementation uses SSE, SSE2, and SSE4.1 instructions. It is not intended for normal use.
* See countWithinDistance which automatically uses the fastest implementation.
*/
std::size_t
countWithinDistanceSSE (const Eigen::VectorXf &model_coefficients,
const double threshold,
std::size_t i = 0) const;
#endif
#if defined (__AVX__) && defined (__AVX2__)
/** This implementation uses AVX and AVX2 instructions. It is not intended for normal use.
* See countWithinDistance which automatically uses the fastest implementation.
*/
std::size_t
countWithinDistanceAVX (const Eigen::VectorXf &model_coefficients,
const double threshold,
std::size_t i = 0) const;
#endif
};
}
#ifdef PCL_NO_PRECOMPILE
#include <pcl/sample_consensus/impl/sac_model_normal_plane.hpp>
#endif
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
pcl::SampleConsensusModelFromNormals
SampleConsensusModelFromNormals represents the base model class for models that require the use of su...
Definition: sac_model.h:612
pcl::SampleConsensusModelFromNormals::PointCloudNConstPtr
typename pcl::PointCloud< PointNT >::ConstPtr PointCloudNConstPtr
Definition: sac_model.h:614
pcl::SampleConsensusModelFromNormals::PointCloudNPtr
typename pcl::PointCloud< PointNT >::Ptr PointCloudNPtr
Definition: sac_model.h:615
pcl::SampleConsensusModel
SampleConsensusModel represents the base model class.
Definition: sac_model.h:70
pcl::SampleConsensusModel::Ptr
shared_ptr< SampleConsensusModel< PointT > > Ptr
Definition: sac_model.h:77
pcl::SampleConsensusModel::sample_size_
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:588
pcl::SampleConsensusModel::PointCloudConstPtr
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: sac_model.h:73
pcl::SampleConsensusModel::model_name_
std::string model_name_
The model name.
Definition: sac_model.h:550
pcl::SampleConsensusModel::model_size_
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:591
pcl::SampleConsensusModel::PointCloudPtr
typename PointCloud::Ptr PointCloudPtr
Definition: sac_model.h:74
pcl::SampleConsensusModel::ConstPtr
shared_ptr< const SampleConsensusModel< PointT > > ConstPtr
Definition: sac_model.h:78
pcl::SampleConsensusModelNormalPlane
SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface no...
Definition: sac_model_normal_plane.h:77
pcl::SampleConsensusModelNormalPlane::countWithinDistance
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.
Definition: sac_model_normal_plane.hpp:106
pcl::SampleConsensusModelNormalPlane::~SampleConsensusModelNormalPlane
~SampleConsensusModelNormalPlane()
Empty destructor.
Definition: sac_model_normal_plane.h:128
pcl::SampleConsensusModelNormalPlane::selectWithinDistance
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.
Definition: sac_model_normal_plane.hpp:50
pcl::SampleConsensusModelNormalPlane::SampleConsensusModelNormalPlane
SampleConsensusModelNormalPlane(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelNormalPlane.
Definition: sac_model_normal_plane.h:101
pcl::SampleConsensusModelNormalPlane::SampleConsensusModelNormalPlane
SampleConsensusModelNormalPlane(const PointCloudConstPtr &cloud, const Indices &indices, bool random=false)
Constructor for base SampleConsensusModelNormalPlane.
Definition: sac_model_normal_plane.h:116
pcl::SampleConsensusModelNormalPlane::getDistancesToModel
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.
Definition: sac_model_normal_plane.hpp:293
pcl::SampleConsensusModelNormalPlane::countWithinDistanceStandard
std::size_t countWithinDistanceStandard(const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i=0) const
This implementation uses no SIMD instructions.
Definition: sac_model_normal_plane.hpp:130
pcl::SampleConsensusModelNormalPlane::getModelType
pcl::SacModel getModelType() const override
Return a unique id for this model (SACMODEL_NORMAL_PLANE).
Definition: sac_model_normal_plane.h:160
pcl::SampleConsensusModelPlane
SampleConsensusModelPlane defines a model for 3D plane segmentation.
Definition: sac_model_plane.h:143
PCL_MAKE_ALIGNED_OPERATOR_NEW
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
memory.h
Defines functions, macros and traits for allocating and using memory.
pcl
Definition: convolution.h:46
pcl::SacModel
SacModel
Definition: model_types.h:46
pcl::SACMODEL_NORMAL_PLANE
@ SACMODEL_NORMAL_PLANE
Definition: model_types.h:58
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
pcl_macros.h
Defines all the PCL and non-PCL macros used.
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:675