Open3D (C++ API)
0.13.0
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Data Structures | |
class | ICPConvergenceCriteria |
Class that defines the convergence criteria of ICP. More... | |
class | RegistrationResult |
class | TransformationEstimation |
class | TransformationEstimationPointToPlane |
class | TransformationEstimationPointToPoint |
Typedefs | |
typedef std::pair< core::Tensor, core::Tensor > | CorrespondenceSet |
CorrespondenceSet: pair of 2x Int64 tensors of shape {C,}, where C is the number of good correspondences between source and target pointcloud. The first tensor is the source indices, and the second tensor is corresponding target indices. Such that, source[correspondence.first] and target[correspondence.second] is a correspondence pair. More... | |
Functions | |
RegistrationResult | EvaluateRegistration (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_correspondence_distance, const core::Tensor &transformation=core::Tensor::Eye( 4, core::Dtype::Float64, core::Device("CPU:0"))) |
Function for evaluating registration between point clouds. More... | |
RegistrationResult | RegistrationICP (const geometry::PointCloud &source, const geometry::PointCloud &target, double max_correspondence_distance, const core::Tensor &init_source_to_target=core::Tensor::Eye( 4, core::Dtype::Float64, core::Device("CPU:0")), const TransformationEstimation &estimation=TransformationEstimationPointToPoint(), const ICPConvergenceCriteria &criteria=ICPConvergenceCriteria()) |
Functions for ICP registration. More... | |
RegistrationResult | RegistrationMultiScaleICP (const geometry::PointCloud &source, const geometry::PointCloud &target, const std::vector< double > &voxel_sizes, const std::vector< ICPConvergenceCriteria > &criteria_list, const std::vector< double > &max_correspondence_distances, const core::Tensor &init_source_to_target=core::Tensor::Eye( 4, core::Dtype::Float64, core::Device("CPU:0")), const TransformationEstimation &estimation=TransformationEstimationPointToPoint()) |
Functions for Multi-Scale ICP registration. It will run ICP on different voxel level, from coarse to dense. The vector of ICPConvergenceCriteria(relative fitness, relative rmse, max_iterations) contains the stoping condition for each voxel level. The length of voxel_sizes vector, criteria vector, max_correspondence_distances vector must be same, and voxel_sizes must contain positive values in strictly decreasing order [Lower the voxel size, higher is the resolution]. Only the last value of the voxel_sizes vector can be {-1}, as it allows to run on the original scale without downsampling. More... | |
typedef std::pair<core::Tensor, core::Tensor> open3d::t::pipelines::registration::CorrespondenceSet |
CorrespondenceSet: pair of 2x Int64 tensors of shape {C,}, where C is the number of good correspondences between source and target pointcloud. The first tensor is the source indices, and the second tensor is corresponding target indices. Such that, source[correspondence.first] and target[correspondence.second] is a correspondence pair.
RegistrationResult open3d::t::pipelines::registration::EvaluateRegistration | ( | const geometry::PointCloud & | source, |
const geometry::PointCloud & | target, | ||
double | max_correspondence_distance, | ||
const core::Tensor & | transformation = core::Tensor::Eye( 4, core::Dtype::Float64, core::Device("CPU:0")) |
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Function for evaluating registration between point clouds.
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 of dtype Float64 on CPU device. |
RegistrationResult open3d::t::pipelines::registration::RegistrationICP | ( | const geometry::PointCloud & | source, |
const geometry::PointCloud & | target, | ||
double | max_correspondence_distance, | ||
const core::Tensor & | init_source_to_target = core::Tensor::Eye( 4, core::Dtype::Float64, core::Device("CPU:0")) , |
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const TransformationEstimation & | estimation = TransformationEstimationPointToPoint() , |
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const ICPConvergenceCriteria & | criteria = ICPConvergenceCriteria() |
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Functions for ICP registration.
source | The source point cloud. |
target | The target point cloud. |
max_correspondence_distance | Maximum correspondence points-pair distance. |
init_source_to_target | Initial transformation estimation of type Float64 on CPU. |
estimation | Estimation method. |
criteria | Convergence criteria. |
RegistrationResult open3d::t::pipelines::registration::RegistrationMultiScaleICP | ( | const geometry::PointCloud & | source, |
const geometry::PointCloud & | target, | ||
const std::vector< double > & | voxel_sizes, | ||
const std::vector< ICPConvergenceCriteria > & | criteria_list, | ||
const std::vector< double > & | max_correspondence_distances, | ||
const core::Tensor & | init_source_to_target = core::Tensor::Eye( 4, core::Dtype::Float64, core::Device("CPU:0")) , |
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const TransformationEstimation & | estimation = TransformationEstimationPointToPoint() |
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Functions for Multi-Scale ICP registration. It will run ICP on different voxel level, from coarse to dense. The vector of ICPConvergenceCriteria(relative fitness, relative rmse, max_iterations) contains the stoping condition for each voxel level. The length of voxel_sizes vector, criteria vector, max_correspondence_distances vector must be same, and voxel_sizes must contain positive values in strictly decreasing order [Lower the voxel size, higher is the resolution]. Only the last value of the voxel_sizes vector can be {-1}, as it allows to run on the original scale without downsampling.
source | The source point cloud. |
target | The target point cloud. |
voxel_sizes | VectorDouble of voxel scales of type double. |
criteria_list | Vector of ICPConvergenceCriteria objects for each scale. |
max_correspondence_distances | VectorDouble of maximum correspondence points-pair distances of type double, for each iteration. Must be of same length as voxel_sizes and criterias. |
init_source_to_target | Initial transformation estimation of type Float64 on CPU. |
estimation | Estimation method. |