Data Structures
class	CauchyLoss

class	CorrespondenceChecker
	Base class that checks if two (small) point clouds can be aligned. More...

class	CorrespondenceCheckerBasedOnDistance
	Check if two aligned point clouds are close. More...

class	CorrespondenceCheckerBasedOnEdgeLength
	Check if two point clouds build the polygons with similar edge lengths. More...

class	CorrespondenceCheckerBasedOnNormal
	Class to check if two aligned point clouds have similar normals. More...

class	CorrespondenceCheckerBasedOnSourceRotation
	Class to limit the rotation of the source object. More...

class	FastGlobalRegistrationOption
	Options for FastGlobalRegistration. More...

class	Feature
	Class to store featrues for registration. More...

class	GlobalOptimizationConvergenceCriteria
	Convergence criteria of GlobalOptimization. More...

class	GlobalOptimizationGaussNewton
	Global optimization with Gauss-Newton algorithm. More...

class	GlobalOptimizationLevenbergMarquardt
	Global optimization with Levenberg-Marquardt algorithm. More...

class	GlobalOptimizationMethod
	Base class for global optimization method. More...

class	GlobalOptimizationOption
	Option for GlobalOptimization. More...

class	GMLoss

class	HuberLoss

class	ICPConvergenceCriteria
	Class that defines the convergence criteria of ICP. More...

struct	InformationMatrixReducer

class	L1Loss

class	L2Loss

class	NormalDistributionsTransformOption
	Class that defines options for 3D Normal Distributions Transform registration. More...

class	PoseGraph
	Data structure defining the pose graph. More...

class	PoseGraphEdge
	Edge of PoseGraph. More...

class	PoseGraphNode
	Node of PoseGraph. More...

class	RANSACConvergenceCriteria
	Class that defines the convergence criteria of RANSAC. More...

struct	RANSACCorrespondenceReduction

struct	RegistrationReduction

class	RegistrationResult

class	RobustKernel

class	TransformationEstimation

class	TransformationEstimationForColoredICP

class	TransformationEstimationForGeneralizedICP

class	TransformationEstimationPointToPlane

class	TransformationEstimationPointToPoint

class	TransformationEstimationSymmetric
	Estimates a source-to-target transformation with symmetric ICP. More...

class	TukeyLoss

Typedefs
typedef std::vector< Eigen::Vector2i >	CorrespondenceSet

Enumerations
enum class	TransformationEstimationType { Unspecified = 0 , PointToPoint = 1 , PointToPlane = 2 , ColoredICP = 3 , GeneralizedICP = 4 , SymmetricICP = 5 }

Functions
RegistrationResult	RegistrationColoredICP (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_distance, const Eigen::Matrix4d &init=Eigen::Matrix4d::Identity(), const TransformationEstimationForColoredICP &estimation=TransformationEstimationForColoredICP(), const ICPConvergenceCriteria &criteria=ICPConvergenceCriteria())
	Function for Colored ICP registration.

RegistrationResult	FastGlobalRegistrationBasedOnCorrespondence (const geometry::PointCloud &source, const geometry::PointCloud &target, const CorrespondenceSet &corres, const FastGlobalRegistrationOption &option=FastGlobalRegistrationOption())
	Fast Global Registration based on a given set of correspondences.

RegistrationResult	FastGlobalRegistrationBasedOnFeatureMatching (const geometry::PointCloud &source, const geometry::PointCloud &target, const Feature &source_feature, const Feature &target_feature, const FastGlobalRegistrationOption &option=FastGlobalRegistrationOption())
	Fast Global Registration based on a given set of FPFH features.

std::shared_ptr< Feature >	ComputeFPFHFeature (const geometry::PointCloud &input, const geometry::KDTreeSearchParam &search_param, const std::optional< std::vector< size_t > > &indices)

CorrespondenceSet	CorrespondencesFromFeatures (const Feature &source_features, const Feature &target_features, bool mutual_filter=false, float mutual_consistency_ratio=0.1)
	Function to find correspondences via 1-nearest neighbor feature matching. Target is used to construct a nearest neighbor search object, in order to query source.

RegistrationResult	RegistrationGeneralizedICP (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_correspondence_distance, const Eigen::Matrix4d &init=Eigen::Matrix4d::Identity(), const TransformationEstimationForGeneralizedICP &estimation=TransformationEstimationForGeneralizedICP(), const ICPConvergenceCriteria &criteria=ICPConvergenceCriteria())
	Function for Generalized ICP registration.

std::shared_ptr< PoseGraph >	CreatePoseGraphWithoutInvalidEdges (const PoseGraph &pose_graph, const GlobalOptimizationOption &option)

void	GlobalOptimization (PoseGraph &pose_graph, const GlobalOptimizationMethod &method, const GlobalOptimizationConvergenceCriteria &criteria, const GlobalOptimizationOption &option)

RegistrationResult	RegistrationNDT (const geometry::PointCloud &source, const geometry::PointCloud &target, const NormalDistributionsTransformOption &option=NormalDistributionsTransformOption(), const Eigen::Matrix4d &init=Eigen::Matrix4d::Identity())
	Function for 3D Normal Distributions Transform registration.

RegistrationResult	EvaluateRegistration (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_correspondence_distance, const Eigen::Matrix4d &transformation=Eigen::Matrix4d::Identity())
	Function for evaluating registration between point clouds.

RegistrationResult	RegistrationICP (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_correspondence_distance, const Eigen::Matrix4d &init=Eigen::Matrix4d::Identity(), const TransformationEstimation &estimation=TransformationEstimationPointToPoint(false), const ICPConvergenceCriteria &criteria=ICPConvergenceCriteria())
	Functions for ICP registration.

template<typename T >
void	atomic_min (std::atomic< T > &min_val, const T &val) noexcept

RegistrationResult	RegistrationRANSACBasedOnCorrespondence (const geometry::PointCloud &source, const geometry::PointCloud &target, const CorrespondenceSet &corres, double max_correspondence_distance, const TransformationEstimation &estimation=TransformationEstimationPointToPoint(false), int ransac_n=3, const std::vector< std::reference_wrapper< const CorrespondenceChecker > > &checkers={}, const RANSACConvergenceCriteria &criteria=RANSACConvergenceCriteria())
	Function for global RANSAC registration based on a given set of correspondences.

RegistrationResult	RegistrationRANSACBasedOnFeatureMatching (const geometry::PointCloud &source, const geometry::PointCloud &target, const Feature &source_features, const Feature &target_features, bool mutual_filter, double max_correspondence_distance, const TransformationEstimation &estimation=TransformationEstimationPointToPoint(false), int ransac_n=3, const std::vector< std::reference_wrapper< const CorrespondenceChecker > > &checkers={}, const RANSACConvergenceCriteria &criteria=RANSACConvergenceCriteria())
	Function for global RANSAC registration based on feature matching.

Eigen::Matrix6d	GetInformationMatrixFromPointClouds (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_correspondence_distance, const Eigen::Matrix4d &transformation)

RegistrationResult	RegistrationSymmetricICP (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_correspondence_distance, const Eigen::Matrix4d &init=Eigen::Matrix4d::Identity(), const TransformationEstimationSymmetric &estimation=TransformationEstimationSymmetric(), const ICPConvergenceCriteria &criteria=ICPConvergenceCriteria())
	Registers source to target with symmetric point-to-plane ICP.

Eigen::Matrix4d	TransformSymmetricPoseToMatrix4d (const Eigen::Vector6d &pose, const Eigen::Vector3d &source_mean, const Eigen::Vector3d &target_mean)

Typedef Documentation

◆ CorrespondenceSet

typedef std::vector< Eigen::Vector2i > open3d::pipelines::registration::CorrespondenceSet

Enumeration Type Documentation

◆ TransformationEstimationType

enum class open3d::pipelines::registration::TransformationEstimationType

strong

Enumerator
Unspecified
PointToPoint
PointToPlane
ColoredICP
GeneralizedICP
SymmetricICP

Function Documentation

◆ atomic_min()

template<typename T >

void open3d::pipelines::registration::atomic_min	(	std::atomic< T > &	min_val,
		const T &	val
	)

noexcept

Atomically store val into min_val if it is smaller. Retries while the compare-exchange loses a race against another thread (which refreshes prev_val), and stops once another thread has already stored a value <= val.

Relaxed ordering is sufficient: min_val is only an optimization hint (the RANSAC iteration bound) and does not publish any other data, so no happens-before relationship with surrounding accesses is required.

◆ ComputeFPFHFeature()

std::shared_ptr< Feature > open3d::pipelines::registration::ComputeFPFHFeature	(	const geometry::PointCloud &	input,
		const geometry::KDTreeSearchParam &	search_param = `geometry::KDTreeSearchParamKNN()`,
		const std::optional< std::vector< size_t > > &	indices = `std::nullopt`
	)

Function to compute FPFH feature for a point cloud.

Parameters

input	The Input point cloud.
search_param	KDTree KNN search parameter.
indices	Indices of the points to compute FPFH features on. If not set, compute features for the whole point cloud.

◆ CorrespondencesFromFeatures()

CorrespondenceSet open3d::pipelines::registration::CorrespondencesFromFeatures	(	const Feature &	source_features,
		const Feature &	target_features,
		bool	mutual_filter = `false`,
		float	mutual_consistency_ratio = `0.1`
	)

Function to find correspondences via 1-nearest neighbor feature matching. Target is used to construct a nearest neighbor search object, in order to query source.

Parameters

source_features	(D, N) feature
target_features	(D, M) feature
mutual_filter	Boolean flag, only return correspondences (i, j) s.t. source_features[i] and target_features[j] are mutually the nearest neighbor.
mutual_consistency_ratio	Float threshold to decide whether the number of correspondences is sufficient. Only used when mutual_filter is set to True.

Returns: A CorrespondenceSet. When mutual_filter is disabled: the first column is arange(0, N) of source, and the second column is the corresponding index of target. When mutual_filter is enabled, return the filtering subset of the aforementioned correspondence set where source[i] and target[j] are mutually the nearest neighbor. If the subset size is smaller than mutual_consistency_ratio * N, return the unfiltered set.

◆ CreatePoseGraphWithoutInvalidEdges()

std::shared_ptr< PoseGraph > open3d::pipelines::registration::CreatePoseGraphWithoutInvalidEdges	(	const PoseGraph &	pose_graph,
		const GlobalOptimizationOption &	option
	)

Function to prune out uncertain edges having confidence_ < .edge_prune_threshold_

◆ EvaluateRegistration()

RegistrationResult open3d::pipelines::registration::EvaluateRegistration	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		double	max_correspondence_distance,
		const Eigen::Matrix4d &	transformation = `Eigen::Matrix4d::Identity()`
	)

Function for evaluating registration between point clouds.

Parameters

source	The source point cloud.
target	The target point cloud.
max_correspondence_distance	Maximum correspondence points-pair distance.
transformation	The 4x4 transformation matrix to transform source to target. Default value: array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]).

◆ FastGlobalRegistrationBasedOnCorrespondence()

RegistrationResult open3d::pipelines::registration::FastGlobalRegistrationBasedOnCorrespondence	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		const CorrespondenceSet &	corres,
		const FastGlobalRegistrationOption &	option = `FastGlobalRegistrationOption()`
	)

Fast Global Registration based on a given set of correspondences.

Parameters

source	The source point cloud.
target	The target point cloud.
corres	Correspondence indices between source and target point clouds.
option	FGR options

◆ FastGlobalRegistrationBasedOnFeatureMatching()

RegistrationResult open3d::pipelines::registration::FastGlobalRegistrationBasedOnFeatureMatching	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		const Feature &	source_feature,
		const Feature &	target_feature,
		const FastGlobalRegistrationOption &	option = `FastGlobalRegistrationOption()`
	)

Fast Global Registration based on a given set of FPFH features.

Parameters

source	The source point cloud.
target	The target point cloud.
corres	Correspondence indices between source and target point clouds.
option	FGR options

◆ GetInformationMatrixFromPointClouds()

Eigen::Matrix6d open3d::pipelines::registration::GetInformationMatrixFromPointClouds	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		double	max_correspondence_distance,
		const Eigen::Matrix4d &	transformation
	)

Parameters

source	The source point cloud.
target	The target point cloud.
max_correspondence_distance	Maximum correspondence points-pair distance.
transformation	The 4x4 transformation matrix to transform `source` to `target`.

◆ GlobalOptimization()

void open3d::pipelines::registration::GlobalOptimization	(	PoseGraph &	pose_graph,
		const GlobalOptimizationMethod &	method = `GlobalOptimizationLevenbergMarquardt()`,
		const GlobalOptimizationConvergenceCriteria &	criteria = `GlobalOptimizationConvergenceCriteria()`,
		const GlobalOptimizationOption &	option = `GlobalOptimizationOption()`
	)

Function to optimize a PoseGraph Reference: [Kümmerle et al 2011] R Kümmerle, G. Grisetti, H. Strasdat, K. Konolige, W. Burgard g2o: A General Framework for Graph Optimization, ICRA 2011 [Choi et al 2015] S. Choi, Q.-Y. Zhou, V. Koltun, Robust Reconstruction of Indoor Scenes, CVPR 2015 [M. Lourakis 2009] M. Lourakis, SBA: A Software Package for Generic Sparse Bundle Adjustment, Transactions on Mathematical Software, 2009

◆ RegistrationColoredICP()

RegistrationResult open3d::pipelines::registration::RegistrationColoredICP	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		double	max_distance,
		const Eigen::Matrix4d &	init = `Eigen::Matrix4d::Identity()`,
		const TransformationEstimationForColoredICP &	estimation = `TransformationEstimationForColoredICP()`,
		const ICPConvergenceCriteria &	criteria = `ICPConvergenceCriteria()`
	)

Function for Colored ICP registration.

This is implementation of following paper J. Park, Q.-Y. Zhou, V. Koltun, Colored Point Cloud Registration Revisited, ICCV 2017.

Parameters

source	The source point cloud.
target	The target point cloud.
max_distance	Maximum correspondence points-pair distance.
init	Initial transformation estimation. Default value: array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]).
estimation	TransformationEstimationForColoredICP method. Can only change the lambda_geometric value and the robust kernel used in the optimization
criteria	Convergence criteria.

◆ RegistrationGeneralizedICP()

RegistrationResult open3d::pipelines::registration::RegistrationGeneralizedICP	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		double	max_correspondence_distance,
		const Eigen::Matrix4d &	init = `Eigen::Matrix4d::Identity()`,
		const TransformationEstimationForGeneralizedICP &	estimation = `TransformationEstimationForGeneralizedICP()`,
		const ICPConvergenceCriteria &	criteria = `ICPConvergenceCriteria()`
	)

Function for Generalized ICP registration.

This is implementation of following paper Generalized-ICP, RSS 2009.

Parameters

source	The source point cloud.
target	The target point cloud.
max_distance	Maximum correspondence points-pair distance.
init	Initial transformation estimation. Default value: array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]).
estimation	Estimation method for transformation.
criteria	Convergence criteria.

◆ RegistrationICP()

RegistrationResult open3d::pipelines::registration::RegistrationICP	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		double	max_correspondence_distance,
		const Eigen::Matrix4d &	init = `Eigen::Matrix4d::Identity()`,
		const TransformationEstimation &	estimation = `TransformationEstimationPointToPoint(false)`,
		const ICPConvergenceCriteria &	criteria = `ICPConvergenceCriteria()`
	)

Functions for ICP registration.

Parameters

source	The source point cloud.
target	The target point cloud.
max_correspondence_distance	Maximum correspondence points-pair distance.
init	Initial transformation estimation. Default value: array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])
estimation	Estimation method.
criteria	Convergence criteria.

◆ RegistrationNDT()

RegistrationResult open3d::pipelines::registration::RegistrationNDT	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		const NormalDistributionsTransformOption &	option = `NormalDistributionsTransformOption()`,
		const Eigen::Matrix4d &	init = `Eigen::Matrix4d::Identity()`
	)

Function for 3D Normal Distributions Transform registration.

This implementation builds a voxelized Gaussian model from the target point cloud and optimizes the rigid source-to-target transformation with Gauss-Newton iterations. The returned correspondence set uses the target point closest to each accepted voxel mean as its representative, and the inlier RMSE is computed from those representative point correspondences.

Parameters

source	The source point cloud.
target	The target point cloud.
option	NDT voxel model and optimization options.
init	Initial transformation estimation.

◆ RegistrationRANSACBasedOnCorrespondence()

RegistrationResult open3d::pipelines::registration::RegistrationRANSACBasedOnCorrespondence	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		const CorrespondenceSet &	corres,
		double	max_correspondence_distance,
		const TransformationEstimation &	estimation = `TransformationEstimationPointToPoint(false)`,
		int	ransac_n = `3`,
		const std::vector< std::reference_wrapper< const CorrespondenceChecker > > &	checkers = `{}`,
		const RANSACConvergenceCriteria &	criteria = `RANSACConvergenceCriteria()`
	)

Function for global RANSAC registration based on a given set of correspondences.

Parameters

source	The source point cloud.
target	The target point cloud.
corres	Correspondence indices between source and target point clouds.
max_correspondence_distance	Maximum correspondence points-pair distance.
estimation	Estimation method.
ransac_n	Fit ransac with `ransac_n` correspondences.
checkers	Correspondence checker.
criteria	Convergence criteria.

◆ RegistrationRANSACBasedOnFeatureMatching()

RegistrationResult open3d::pipelines::registration::RegistrationRANSACBasedOnFeatureMatching	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		const Feature &	source_features,
		const Feature &	target_features,
		bool	mutual_filter,
		double	max_correspondence_distance,
		const TransformationEstimation &	estimation = `TransformationEstimationPointToPoint(false)`,
		int	ransac_n = `3`,
		const std::vector< std::reference_wrapper< const CorrespondenceChecker > > &	checkers = `{}`,
		const RANSACConvergenceCriteria &	criteria = `RANSACConvergenceCriteria()`
	)

Function for global RANSAC registration based on feature matching.

Parameters

source	The source point cloud.
target	The target point cloud.
source_features	Source point cloud feature.
target_features	Target point cloud feature.
mutual_filter	Enables mutual filter such that the correspondence of the source point's correspondence is itself.
max_correspondence_distance	Maximum correspondence points-pair distance.
ransac_n	Fit ransac with `ransac_n` correspondences.
checkers	Correspondence checker.
criteria	Convergence criteria.

◆ RegistrationSymmetricICP()

RegistrationResult open3d::pipelines::registration::RegistrationSymmetricICP	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		double	max_correspondence_distance,
		const Eigen::Matrix4d &	init = `Eigen::Matrix4d::Identity()`,
		const TransformationEstimationSymmetric &	estimation = `TransformationEstimationSymmetric()`,
		const ICPConvergenceCriteria &	criteria = `ICPConvergenceCriteria()`
	)

Registers source to target with symmetric point-to-plane ICP.

For each correspondence in the current aligned frame, \(p\) and \(n_p\) denote the source point and normal, while \(q\) and \(n_q\) denote the target point and normal. After aligning normal directions, the objective uses the single residual \((p-q)^T(n_p+n_q)\).

Parameters

source	Source point cloud with normals.
target	Target point cloud with normals.
max_correspondence_distance	Maximum correspondence distance.
init	Initial source-to-target transformation.
estimation	Symmetric transformation estimator.
criteria	ICP convergence criteria.

Returns: The registration result with a source-to-target transformation.

Exceptions

std::runtime_error If either point cloud lacks normals.

◆ TransformSymmetricPoseToMatrix4d()

Eigen::Matrix4d open3d::pipelines::registration::TransformSymmetricPoseToMatrix4d	(	const Eigen::Vector6d &	pose,
		const Eigen::Vector3d &	source_mean,
		const Eigen::Vector3d &	target_mean
	)

inline

Data Structures

Typedefs

Enumerations

Functions

Typedef Documentation

◆ CorrespondenceSet

Enumeration Type Documentation

◆ TransformationEstimationType

Function Documentation

◆ atomic_min()

◆ ComputeFPFHFeature()

◆ CorrespondencesFromFeatures()

◆ CreatePoseGraphWithoutInvalidEdges()

◆ EvaluateRegistration()

◆ FastGlobalRegistrationBasedOnCorrespondence()

◆ FastGlobalRegistrationBasedOnFeatureMatching()

◆ GetInformationMatrixFromPointClouds()

◆ GlobalOptimization()

◆ RegistrationColoredICP()

◆ RegistrationGeneralizedICP()

◆ RegistrationICP()

◆ RegistrationNDT()

◆ RegistrationRANSACBasedOnCorrespondence()

◆ RegistrationRANSACBasedOnFeatureMatching()

◆ RegistrationSymmetricICP()

◆ TransformSymmetricPoseToMatrix4d()