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	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...

class	L1Loss

class	L2Loss

class	PointCloudForColoredICP

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...

class	RegistrationResult

class	RobustKernel

class	TransformationEstimation

class	TransformationEstimationForColoredICP

class	TransformationEstimationPointToPlane

class	TransformationEstimationPointToPoint

class	TukeyLoss

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

Enumerations
enum	TransformationEstimationType { TransformationEstimationType::Unspecified = 0, TransformationEstimationType::PointToPoint = 1, TransformationEstimationType::PointToPlane = 2, TransformationEstimationType::ColoredICP = 3 }

Functions
RegistrationResult	RegistrationColoredICP (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_distance, const Eigen::Matrix4d &init=Eigen::Matrix4d::Identity(), const ICPConvergenceCriteria &criteria=ICPConvergenceCriteria(), double lambda_geometric=0.968)
	Function for Colored ICP registration. More...

RegistrationResult	FastGlobalRegistration (const geometry::PointCloud &source, const geometry::PointCloud &target, const Feature &source_feature, const Feature &target_feature, const FastGlobalRegistrationOption &option)

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

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	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. More...

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. More...

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=6, const RANSACConvergenceCriteria &criteria=RANSACConvergenceCriteria())
	Function for global RANSAC registration based on a given set of correspondences. More...

RegistrationResult	RegistrationRANSACBasedOnFeatureMatching (const geometry::PointCloud &source, const geometry::PointCloud &target, const Feature &source_feature, const Feature &target_feature, double max_correspondence_distance, const TransformationEstimation &estimation=TransformationEstimationPointToPoint(false), int ransac_n=4, const std::vector< std::reference_wrapper< const CorrespondenceChecker >> &checkers={}, const RANSACConvergenceCriteria &criteria=RANSACConvergenceCriteria())
	Function for global RANSAC registration based on feature matching. More...

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

Typedef Documentation

◆ CorrespondenceSet

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

Enumeration Type Documentation

◆ TransformationEstimationType

enum open3d::pipelines::registration::TransformationEstimationType

strong

Enumerator
Unspecified
PointToPoint
PointToPlane
ColoredICP

Function Documentation

◆ ComputeFPFHFeature()

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

Function to compute FPFH feature for a point cloud.

Parameters

input	The Input point cloud.
search_param	KDTree KNN search parameter.

◆ 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.]]).

◆ FastGlobalRegistration()

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

◆ 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 ICPConvergenceCriteria &	criteria = `ICPConvergenceCriteria()`,
		double	lambda_geometric = `0.968`
	)

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.]]).
criteria	Convergence criteria.
lambda_geometric	lambda_geometric value.

◆ 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.

◆ 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 = `6`,
		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	Checker class to check if two point clouds can be aligned.
max_correspondence_distance	Maximum correspondence points-pair distance.
estimation	Estimation method.
ransac_n	Fit ransac with `ransac_n` correspondences.
criteria	Convergence criteria.

◆ RegistrationRANSACBasedOnFeatureMatching()

RegistrationResult open3d::pipelines::registration::RegistrationRANSACBasedOnFeatureMatching	(	const geometry::PointCloud &	source,
		const geometry::PointCloud &	target,
		const Feature &	source_feature,
		const Feature &	target_feature,
		double	max_correspondence_distance,
		const TransformationEstimation &	estimation = `TransformationEstimationPointToPoint(false)`,
		int	ransac_n = `4`,
		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_feature	Source point cloud feature.
target_feature	Target point cloud feature.
max_correspondence_distance	Maximum correspondence points-pair distance.
ransac_n	Fit ransac with `ransac_n` correspondences.
checkers	Correspondence checker.
criteria	Convergence criteria.

Data Structures

Typedefs

Enumerations

Functions

Typedef Documentation

◆ CorrespondenceSet

Enumeration Type Documentation

◆ TransformationEstimationType

Function Documentation

◆ ComputeFPFHFeature()

◆ CreatePoseGraphWithoutInvalidEdges()

◆ EvaluateRegistration()

◆ FastGlobalRegistration()

◆ GetInformationMatrixFromPointClouds()

◆ GlobalOptimization()

◆ RegistrationColoredICP()

◆ RegistrationICP()

◆ RegistrationRANSACBasedOnCorrespondence()

◆ RegistrationRANSACBasedOnFeatureMatching()