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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include "open3d/t/pipelines/registration/Registration.h"
#include <memory>
#include <utility>
#include "open3d/t/geometry/PointCloud.h"
#include "open3d/t/pipelines/registration/TransformationEstimation.h"
#include "open3d/utility/Logging.h"
#include "pybind/docstring.h"
#include "pybind/t/pipelines/registration/registration.h"
namespace open3d {
namespace t {
namespace pipelines {
namespace registration {
template <class TransformationEstimationBase = TransformationEstimation>
class PyTransformationEstimation : public TransformationEstimationBase {
public:
using TransformationEstimationBase::TransformationEstimationBase;
TransformationEstimationType GetTransformationEstimationType() const {
PYBIND11_OVERLOAD_PURE(TransformationEstimationType,
TransformationEstimationBase, void);
}
double ComputeRMSE(const t::geometry::PointCloud &source,
const t::geometry::PointCloud &target,
const core::Tensor &correspondences) const {
PYBIND11_OVERLOAD_PURE(double, TransformationEstimationBase, source,
target, correspondences);
}
core::Tensor ComputeTransformation(const t::geometry::PointCloud &source,
const t::geometry::PointCloud &target,
const core::Tensor &correspondences,
const core::Tensor ¤t_transform,
const std::size_t iteration) const {
PYBIND11_OVERLOAD_PURE(core::Tensor, TransformationEstimationBase,
source, target, correspondences,
current_transform, iteration);
}
};
// Registration functions have similar arguments, sharing arg docstrings.
static const std::unordered_map<std::string, std::string>
map_shared_argument_docstrings = {
{"correspondences",
"Tensor of type Int64 containing indices of corresponding "
"target points, where the value is the target index and the "
"index of the value itself is the source index. It contains "
"-1 as value at index with no correspondence."},
{"criteria", "Convergence criteria"},
{"criteria_list",
"List of Convergence criteria for each scale of multi-scale "
"icp."},
{"estimation_method",
"Estimation method. One of "
"(``TransformationEstimationPointToPoint``, "
"``TransformationEstimationPointToPlane``, "
"``TransformationEstimationForColoredICP``, "
"``TransformationEstimationForGeneralizedICP``)"},
{"init_source_to_target", "Initial transformation estimation"},
{"max_correspondence_distance",
"Maximum correspondence points-pair distance."},
{"max_correspondence_distances",
"o3d.utility.DoubleVector of maximum correspondence "
"points-pair distances for multi-scale icp."},
{"option", "Registration option"},
{"source", "The source point cloud."},
{"target", "The target point cloud."},
{"transformation",
"The 4x4 transformation matrix of type Float64 "
"to transform ``source`` to ``target``"},
{"voxel_size",
"The input pointclouds will be down-sampled to this "
"`voxel_size` scale. If `voxel_size` < 0, original scale will "
"be used. However it is highly recommended to down-sample the "
"point-cloud for performance. By default original scale of "
"the point-cloud will be used."},
{"voxel_sizes",
"o3d.utility.DoubleVector of voxel sizes in strictly "
"decreasing order, for multi-scale icp."},
{"callback_after_iteration",
"Optional lambda function, saves string to tensor map of "
"attributes such as iteration_index, scale_index, "
"scale_iteration_index, inlier_rmse, fitness, transformation, "
"on CPU device, updated after each iteration."}};
void pybind_registration_declarations(py::module &m) {
py::module m_registration = m.def_submodule(
"registration", "Tensor-based registration pipeline.");
py::class_<ICPConvergenceCriteria> convergence_criteria(
m_registration, "ICPConvergenceCriteria",
"Convergence criteria of ICP. "
"ICP algorithm stops if the relative change of fitness and rmse "
"hit ``relative_fitness`` and ``relative_rmse`` individually, "
"or the iteration number exceeds ``max_iteration``.");
py::class_<RegistrationResult> registration_result(
m_registration, "RegistrationResult", "Registration results.");
py::class_<TransformationEstimation,
PyTransformationEstimation<TransformationEstimation>>
te(m_registration, "TransformationEstimation",
"Base class that estimates a transformation between two "
"point clouds. The virtual function ComputeTransformation() "
"must be implemented in subclasses.");
py::class_<TransformationEstimationPointToPoint,
PyTransformationEstimation<TransformationEstimationPointToPoint>,
TransformationEstimation>
te_p2p(m_registration, "TransformationEstimationPointToPoint",
"Class to estimate a transformation for point to "
"point distance.");
py::class_<TransformationEstimationPointToPlane,
PyTransformationEstimation<TransformationEstimationPointToPlane>,
TransformationEstimation>
te_p2l(m_registration, "TransformationEstimationPointToPlane",
"Class to estimate a transformation for point to "
"plane distance.");
py::class_<
TransformationEstimationForColoredICP,
PyTransformationEstimation<TransformationEstimationForColoredICP>,
TransformationEstimation>
te_col(m_registration, "TransformationEstimationForColoredICP",
"Class to estimate a transformation between two point "
"clouds using color information");
py::class_<
TransformationEstimationForDopplerICP,
PyTransformationEstimation<TransformationEstimationForDopplerICP>,
TransformationEstimation>
te_dop(m_registration, "TransformationEstimationForDopplerICP",
"Class to estimate a transformation between two point "
"clouds using color information");
pybind_robust_kernel_declarations(m_registration);
}
void pybind_registration_definitions(py::module &m) {
auto m_registration = static_cast<py::module>(m.attr("registration"));
// open3d.t.pipelines.registration.ICPConvergenceCriteria
auto convergence_criteria = static_cast<py::class_<ICPConvergenceCriteria>>(
m_registration.attr("ICPConvergenceCriteria"));
py::detail::bind_copy_functions<ICPConvergenceCriteria>(
convergence_criteria);
convergence_criteria
.def(py::init<double, double, int>(), "relative_fitness"_a = 1e-6,
"relative_rmse"_a = 1e-6, "max_iteration"_a = 30)
.def_readwrite(
"relative_fitness",
&ICPConvergenceCriteria::relative_fitness_,
"If relative change (difference) of fitness score is lower "
"than ``relative_fitness``, the iteration stops.")
.def_readwrite(
"relative_rmse", &ICPConvergenceCriteria::relative_rmse_,
"If relative change (difference) of inlier RMSE score is "
"lower than ``relative_rmse``, the iteration stops.")
.def_readwrite("max_iteration",
&ICPConvergenceCriteria::max_iteration_,
"Maximum iteration before iteration stops.")
.def("__repr__", [](const ICPConvergenceCriteria &c) {
return fmt::format(
"ICPConvergenceCriteria[relative_fitness_={:e}, "
"relative_rmse={:e}, max_iteration_={:d}].",
c.relative_fitness_, c.relative_rmse_,
c.max_iteration_);
});
// open3d.t.pipelines.registration.RegistrationResult
auto registration_result = static_cast<py::class_<RegistrationResult>>(
m_registration.attr("RegistrationResult"));
py::detail::bind_default_constructor<RegistrationResult>(
registration_result);
py::detail::bind_copy_functions<RegistrationResult>(registration_result);
registration_result
.def_readwrite("transformation",
&RegistrationResult::transformation_,
"``4 x 4`` float64 tensor on CPU: The estimated "
"transformation matrix.")
.def_readwrite("correspondence_set",
&RegistrationResult::correspondences_,
"Tensor of type Int64 containing indices of "
"corresponding target points, where the value is "
"the target index and the index of the value itself "
"is the source index. It contains -1 as value at "
"index with no correspondence.")
.def_readwrite("inlier_rmse", &RegistrationResult::inlier_rmse_,
"float: RMSE of all inlier correspondences. Lower "
"is better.")
.def_readwrite("fitness", &RegistrationResult::fitness_,
"float: The overlapping area (# of inlier "
"correspondences "
"/ # of points in source). Higher is better.")
.def_readwrite(
"converged", &RegistrationResult::converged_,
"bool: Specifies whether the algorithm converged or not.")
.def_readwrite(
"num_iterations", &RegistrationResult::num_iterations_,
"int: Number of iterations the algorithm took to converge.")
.def("__repr__", [](const RegistrationResult &rr) {
return fmt::format(
"RegistrationResult["
"converged={}"
", num_iteration={:d}"
", fitness_={:e}"
", inlier_rmse={:e}"
", correspondences={:d}]."
"\nAccess transformation to get result.",
rr.converged_, rr.num_iterations_, rr.fitness_,
rr.inlier_rmse_, rr.correspondences_.GetLength());
});
// open3d.t.pipelines.registration.TransformationEstimation
auto te = static_cast<
py::class_<TransformationEstimation,
PyTransformationEstimation<TransformationEstimation>>>(
m_registration.attr("TransformationEstimation"));
te.def("compute_rmse", &TransformationEstimation::ComputeRMSE, "source"_a,
"target"_a, "correspondences"_a,
"Compute RMSE between source and target points cloud given "
"correspondences.");
te.def("compute_transformation",
&TransformationEstimation::ComputeTransformation, "source"_a,
"target"_a, "correspondences"_a,
"current_transform"_a =
core::Tensor::Eye(4, core::Float64, core::Device("CPU:0")),
"iteration"_a = 0,
"Compute transformation from source to target point cloud given "
"correspondences.");
docstring::ClassMethodDocInject(m_registration, "TransformationEstimation",
"compute_rmse",
{{"source", "Source point cloud."},
{"target", "Target point cloud."},
{"correspondences",
"Tensor of type Int64 containing "
"indices of corresponding target "
"points, where the value is the "
"target index and the index of "
"the value itself is the source "
"index. It contains -1 as value "
"at index with no correspondence."}});
docstring::ClassMethodDocInject(
m_registration, "TransformationEstimation",
"compute_transformation",
{{"source", "Source point cloud."},
{"target", "Target point cloud."},
{"correspondences",
"Tensor of type Int64 containing indices of corresponding target "
"points, where the value is the target index and the index of "
"the value itself is the source index. It contains -1 as value "
"at index with no correspondence."},
{"current_transform", "The current pose estimate of ICP."},
{"iteration",
"The current iteration number of the ICP algorithm."}});
// open3d.t.pipelines.registration.TransformationEstimationPointToPoint
// TransformationEstimation
auto te_p2p = static_cast<py::class_<
TransformationEstimationPointToPoint,
PyTransformationEstimation<TransformationEstimationPointToPoint>,
TransformationEstimation>>(
m_registration.attr("TransformationEstimationPointToPoint"));
py::detail::bind_copy_functions<TransformationEstimationPointToPoint>(
te_p2p);
te_p2p.def(py::init())
.def("__repr__",
[](const TransformationEstimationPointToPoint &te) {
return std::string("TransformationEstimationPointToPoint");
});
// open3d.t.pipelines.registration.TransformationEstimationPointToPlane
// TransformationEstimation
auto te_p2l = static_cast<py::class_<
TransformationEstimationPointToPlane,
PyTransformationEstimation<TransformationEstimationPointToPlane>,
TransformationEstimation>>(
m_registration.attr("TransformationEstimationPointToPlane"));
py::detail::bind_default_constructor<TransformationEstimationPointToPlane>(
te_p2l);
py::detail::bind_copy_functions<TransformationEstimationPointToPlane>(
te_p2l);
te_p2l.def(py::init([](const RobustKernel &kernel) {
return new TransformationEstimationPointToPlane(kernel);
}),
"kernel"_a)
.def("__repr__",
[](const TransformationEstimationPointToPlane &te) {
return std::string("TransformationEstimationPointToPlane");
})
.def_readwrite("kernel",
&TransformationEstimationPointToPlane::kernel_,
"Robust Kernel used in the Optimization");
// open3d.t.pipelines.registration.TransformationEstimationForColoredICP
// TransformationEstimation
auto te_col = static_cast<py::class_<
TransformationEstimationForColoredICP,
PyTransformationEstimation<TransformationEstimationForColoredICP>,
TransformationEstimation>>(
m_registration.attr("TransformationEstimationForColoredICP"));
py::detail::bind_default_constructor<TransformationEstimationForColoredICP>(
te_col);
py::detail::bind_copy_functions<TransformationEstimationForColoredICP>(
te_col);
te_col.def(py::init([](double lambda_geometric, RobustKernel &kernel) {
return new TransformationEstimationForColoredICP(
lambda_geometric, kernel);
}),
"lambda_geometric"_a, "kernel"_a)
.def(py::init([](const double lambda_geometric) {
return new TransformationEstimationForColoredICP(
lambda_geometric);
}),
"lambda_geometric"_a)
.def(py::init([](const RobustKernel kernel) {
auto te = TransformationEstimationForColoredICP();
te.kernel_ = kernel;
return te;
}),
"kernel"_a)
.def("__repr__",
[](const TransformationEstimationForColoredICP &te) {
return std::string(
"TransformationEstimationForColoredICP "
"with lambda_geometric: ") +
std::to_string(te.lambda_geometric_);
})
.def_readwrite(
"lambda_geometric",
&TransformationEstimationForColoredICP::lambda_geometric_,
"lambda_geometric")
.def_readwrite("kernel",
&TransformationEstimationForColoredICP::kernel_,
"Robust Kernel used in the Optimization");
// open3d.t.pipelines.registration.TransformationEstimationForDopplerICP
// TransformationEstimation
auto te_dop = static_cast<py::class_<
TransformationEstimationForDopplerICP,
PyTransformationEstimation<TransformationEstimationForDopplerICP>,
TransformationEstimation>>(
m_registration.attr("TransformationEstimationForDopplerICP"));
py::detail::bind_default_constructor<TransformationEstimationForDopplerICP>(
te_dop);
py::detail::bind_copy_functions<TransformationEstimationForDopplerICP>(
te_dop);
te_dop.def(py::init([](double period, double lambda_doppler,
bool reject_dynamic_outliers,
double doppler_outlier_threshold,
std::size_t outlier_rejection_min_iteration,
std::size_t geometric_robust_loss_min_iteration,
std::size_t doppler_robust_loss_min_iteration,
RobustKernel &geometric_kernel,
RobustKernel &doppler_kernel,
core::Tensor &transform_vehicle_to_sensor) {
return new TransformationEstimationForDopplerICP(
period, lambda_doppler, reject_dynamic_outliers,
doppler_outlier_threshold,
outlier_rejection_min_iteration,
geometric_robust_loss_min_iteration,
doppler_robust_loss_min_iteration, geometric_kernel,
doppler_kernel, transform_vehicle_to_sensor);
}),
"period"_a, "lambda_doppler"_a, "reject_dynamic_outliers"_a,
"doppler_outlier_threshold"_a,
"outlier_rejection_min_iteration"_a,
"geometric_robust_loss_min_iteration"_a,
"doppler_robust_loss_min_iteration"_a, "goemetric_kernel"_a,
"doppler_kernel"_a, "transform_vehicle_to_sensor"_a)
.def(py::init([](const double lambda_doppler) {
return new TransformationEstimationForDopplerICP(
lambda_doppler);
}),
"lambda_doppler"_a)
.def("compute_transformation",
py::overload_cast<const t::geometry::PointCloud &,
const t::geometry::PointCloud &,
const core::Tensor &, const core::Tensor &,
const std::size_t>(
&TransformationEstimationForDopplerICP::
ComputeTransformation,
py::const_),
"Compute transformation from source to target point cloud "
"given correspondences")
.def("__repr__",
[](const TransformationEstimationForDopplerICP &te) {
return std::string(
"TransformationEstimationForDopplerICP "
"with lambda_doppler: ") +
std::to_string(te.lambda_doppler_);
})
.def_readwrite("period",
&TransformationEstimationForDopplerICP::period_,
"Time period (in seconds) between the source and "
"the target point clouds.")
.def_readwrite(
"lambda_doppler",
&TransformationEstimationForDopplerICP::lambda_doppler_,
"`λ ∈ [0, 1]` in the overall energy `(1−λ)EG + λED`. Refer "
"the documentation of DopplerICP for more information.")
.def_readwrite("reject_dynamic_outliers",
&TransformationEstimationForDopplerICP::
reject_dynamic_outliers_,
"Whether or not to reject dynamic point outlier "
"correspondences.")
.def_readwrite("doppler_outlier_threshold",
&TransformationEstimationForDopplerICP::
doppler_outlier_threshold_,
"Correspondences with Doppler error greater than "
"this threshold are rejected from optimization.")
.def_readwrite("outlier_rejection_min_iteration",
&TransformationEstimationForDopplerICP::
outlier_rejection_min_iteration_,
"Number of iterations of ICP after which outlier "
"rejection is enabled.")
.def_readwrite("geometric_robust_loss_min_iteration",
&TransformationEstimationForDopplerICP::
geometric_robust_loss_min_iteration_,
"Minimum iterations after which Robust Kernel is "
"used for the Geometric error")
.def_readwrite("doppler_robust_loss_min_iteration",
&TransformationEstimationForDopplerICP::
doppler_robust_loss_min_iteration_,
"Minimum iterations after which Robust Kernel is "
"used for the Doppler error")
.def_readwrite(
"geometric_kernel",
&TransformationEstimationForDopplerICP::geometric_kernel_,
"Robust Kernel used in the Geometric Error Optimization")
.def_readwrite(
"doppler_kernel",
&TransformationEstimationForDopplerICP::doppler_kernel_,
"Robust Kernel used in the Doppler Error Optimization")
.def_readwrite("transform_vehicle_to_sensor",
&TransformationEstimationForDopplerICP::
transform_vehicle_to_sensor_,
"The 4x4 extrinsic transformation matrix between "
"the vehicle and the sensor frames.");
m_registration.def(
"evaluate_registration", &EvaluateRegistration,
py::call_guard<py::gil_scoped_release>(),
"Function for evaluating registration between point clouds",
"source"_a, "target"_a, "max_correspondence_distance"_a,
"transformation"_a =
core::Tensor::Eye(4, core::Float64, core::Device("CPU:0")));
docstring::FunctionDocInject(m_registration, "evaluate_registration",
map_shared_argument_docstrings);
m_registration.def(
"icp", &ICP, py::call_guard<py::gil_scoped_release>(),
"Function for ICP registration", "source"_a, "target"_a,
"max_correspondence_distance"_a,
"init_source_to_target"_a =
core::Tensor::Eye(4, core::Float64, core::Device("CPU:0")),
"estimation_method"_a = TransformationEstimationPointToPoint(),
"criteria"_a = ICPConvergenceCriteria(), "voxel_size"_a = -1.0,
"callback_after_iteration"_a = py::none());
docstring::FunctionDocInject(m_registration, "icp",
map_shared_argument_docstrings);
m_registration.def(
"multi_scale_icp", &MultiScaleICP,
py::call_guard<py::gil_scoped_release>(),
"Function for Multi-Scale ICP registration", "source"_a, "target"_a,
"voxel_sizes"_a, "criteria_list"_a,
"max_correspondence_distances"_a,
"init_source_to_target"_a =
core::Tensor::Eye(4, core::Float64, core::Device("CPU:0")),
"estimation_method"_a = TransformationEstimationPointToPoint(),
"callback_after_iteration"_a = py::none());
docstring::FunctionDocInject(m_registration, "multi_scale_icp",
map_shared_argument_docstrings);
m_registration.def(
"get_information_matrix", &GetInformationMatrix,
py::call_guard<py::gil_scoped_release>(),
"Function for computing information matrix from transformation "
"matrix. Information matrix is tensor of shape {6, 6}, dtype "
"Float64 "
"on CPU device.",
"source"_a, "target"_a, "max_correspondence_distance"_a,
"transformation"_a);
docstring::FunctionDocInject(m_registration, "get_information_matrix",
map_shared_argument_docstrings);
pybind_feature_definitions(m_registration);
pybind_robust_kernel_definitions(m_registration);
}
} // namespace registration
} // namespace pipelines
} // namespace t
} // namespace open3d
|
