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|
// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include "open3d/pipelines/registration/Registration.h"
#include <memory>
#include <utility>
#include "open3d/geometry/PointCloud.h"
#include "open3d/pipelines/registration/ColoredICP.h"
#include "open3d/pipelines/registration/CorrespondenceChecker.h"
#include "open3d/pipelines/registration/FastGlobalRegistration.h"
#include "open3d/pipelines/registration/Feature.h"
#include "open3d/pipelines/registration/GeneralizedICP.h"
#include "open3d/pipelines/registration/RobustKernel.h"
#include "open3d/pipelines/registration/TransformationEstimation.h"
#include "open3d/utility/Logging.h"
#include "pybind/docstring.h"
#include "pybind/pipelines/registration/registration.h"
namespace open3d {
namespace pipelines {
namespace registration {
template <class TransformationEstimationBase = TransformationEstimation>
class PyTransformationEstimation : public TransformationEstimationBase {
public:
using TransformationEstimationBase::TransformationEstimationBase;
TransformationEstimationType GetTransformationEstimationType()
const override {
PYBIND11_OVERLOAD_PURE(TransformationEstimationType,
TransformationEstimationBase, void);
}
double ComputeRMSE(const geometry::PointCloud &source,
const geometry::PointCloud &target,
const CorrespondenceSet &corres) const override {
PYBIND11_OVERLOAD_PURE(double, TransformationEstimationBase, source,
target, corres);
}
Eigen::Matrix4d ComputeTransformation(
const geometry::PointCloud &source,
const geometry::PointCloud &target,
const CorrespondenceSet &corres) const override {
PYBIND11_OVERLOAD_PURE(Eigen::Matrix4d, TransformationEstimationBase,
source, target, corres);
}
};
template <class CorrespondenceCheckerBase = CorrespondenceChecker>
class PyCorrespondenceChecker : public CorrespondenceCheckerBase {
public:
using CorrespondenceCheckerBase::CorrespondenceCheckerBase;
bool Check(const geometry::PointCloud &source,
const geometry::PointCloud &target,
const CorrespondenceSet &corres,
const Eigen::Matrix4d &transformation) const override {
PYBIND11_OVERLOAD_PURE(bool, CorrespondenceCheckerBase, source, target,
corres, transformation);
}
};
void pybind_registration_declarations(py::module &m) {
py::module m_registration =
m.def_submodule("registration", "Registration pipeline.");
py::class_<ICPConvergenceCriteria> convergence_criteria(
m_registration, "ICPConvergenceCriteria",
"Class that defines the 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_<RANSACConvergenceCriteria> ransac_criteria(
m_registration, "RANSACConvergenceCriteria",
"Class that defines the convergence criteria of "
"RANSAC. RANSAC algorithm stops if the iteration "
"number hits ``max_iteration``, or the fitness "
"measured during validation suggests that the "
"algorithm can be terminated early with some "
"``confidence``. Early termination takes place "
"when the number of iterations reaches ``k = "
"log(1 - confidence)/log(1 - fitness^{ransac_n})``, "
"where ``ransac_n`` is the number of points used "
"during a ransac iteration. Use confidence=1.0 "
"to avoid early termination.");
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_<TransformationEstimationForGeneralizedICP,
PyTransformationEstimation<
TransformationEstimationForGeneralizedICP>,
TransformationEstimation>
te_gicp(m_registration, "TransformationEstimationForGeneralizedICP",
"Class to estimate a transformation for Generalized ICP.");
py::class_<CorrespondenceChecker,
PyCorrespondenceChecker<CorrespondenceChecker>>
cc(m_registration, "CorrespondenceChecker",
"Base class that checks if two (small) point clouds can be "
"aligned. This class is used in feature based matching "
"algorithms (such as RANSAC and FastGlobalRegistration) to "
"prune out outlier correspondences. The virtual function "
"Check() must be implemented in subclasses.");
py::class_<CorrespondenceCheckerBasedOnEdgeLength,
PyCorrespondenceChecker<CorrespondenceCheckerBasedOnEdgeLength>,
CorrespondenceChecker>
cc_el(m_registration, "CorrespondenceCheckerBasedOnEdgeLength",
"Check if two point clouds build the polygons with similar "
"edge lengths. That is, checks if the lengths of any two "
"arbitrary edges (line formed by two vertices) individually "
"drawn within the source point cloud and within the target "
"point cloud with correspondences are similar. The only "
"parameter similarity_threshold is a number between 0 "
"(loose) and 1 (strict)");
py::class_<CorrespondenceCheckerBasedOnDistance,
PyCorrespondenceChecker<CorrespondenceCheckerBasedOnDistance>,
CorrespondenceChecker>
cc_d(m_registration, "CorrespondenceCheckerBasedOnDistance",
"Class to check if aligned point clouds are close (less than "
"specified threshold).");
py::class_<CorrespondenceCheckerBasedOnNormal,
PyCorrespondenceChecker<CorrespondenceCheckerBasedOnNormal>,
CorrespondenceChecker>
cc_n(m_registration, "CorrespondenceCheckerBasedOnNormal",
"Class to check if two aligned point clouds have similar "
"normals. It considers vertex normal affinity of any "
"correspondences. It computes dot product of two normal "
"vectors. It takes radian value for the threshold.");
py::class_<FastGlobalRegistrationOption> fgr_option(
m_registration, "FastGlobalRegistrationOption",
"Options for FastGlobalRegistration.");
py::class_<RegistrationResult> registration_result(
m_registration, "RegistrationResult",
"Class that contains the registration results.");
pybind_feature_declarations(m_registration);
pybind_global_optimization_declarations(m_registration);
pybind_robust_kernels_declarations(m_registration);
}
void pybind_registration_definitions(py::module &m) {
auto m_registration = static_cast<py::module>(m.attr("registration"));
// open3d.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 fitness, double rmse, int itr) {
return new ICPConvergenceCriteria(fitness, rmse, itr);
}),
"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 inliner 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.registration.RANSACConvergenceCriteria
auto ransac_criteria = static_cast<py::class_<RANSACConvergenceCriteria>>(
m_registration.attr("RANSACConvergenceCriteria"));
py::detail::bind_copy_functions<RANSACConvergenceCriteria>(ransac_criteria);
ransac_criteria
.def(py::init([](int max_iteration, double confidence) {
return new RANSACConvergenceCriteria(max_iteration,
confidence);
}),
"max_iteration"_a = 100000, "confidence"_a = 0.999)
.def_readwrite("max_iteration",
&RANSACConvergenceCriteria::max_iteration_,
"Maximum iteration before iteration stops.")
.def_readwrite(
"confidence", &RANSACConvergenceCriteria::confidence_,
"Desired probability of success. Used for estimating early "
"termination. Use 1.0 to avoid early termination.")
.def("__repr__", [](const RANSACConvergenceCriteria &c) {
return fmt::format(
"RANSACConvergenceCriteria("
"max_iteration={:d}, "
"confidence={:e})",
c.max_iteration_, c.confidence_);
});
// open3d.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, "corres"_a,
"Compute RMSE between source and target points cloud given "
"correspondences.");
te.def("compute_transformation",
&TransformationEstimation::ComputeTransformation, "source"_a,
"target"_a, "corres"_a,
"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."},
{"corres",
"Correspondence set between source and target point cloud."}});
docstring::ClassMethodDocInject(
m_registration, "TransformationEstimation",
"compute_transformation",
{{"source", "Source point cloud."},
{"target", "Target point cloud."},
{"corres",
"Correspondence set between source and target point cloud."}});
// open3d.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([](bool with_scaling) {
return new TransformationEstimationPointToPoint(
with_scaling);
}),
"with_scaling"_a = false)
.def("__repr__",
[](const TransformationEstimationPointToPoint &te) {
return fmt::format(
"TransformationEstimationPointToPoint("
"with_scaling={})",
te.with_scaling_ ? "True" : "False");
})
.def_readwrite(
"with_scaling",
&TransformationEstimationPointToPoint::with_scaling_,
R"(Set to ``True`` to estimate scaling, ``False`` to force
scaling to be ``1``.
The homogeneous transformation is given by
:math:`T = \begin{bmatrix} c\mathbf{R} & \mathbf{t} \\ \mathbf{0} & 1 \end{bmatrix}`
Sets :math:`c = 1` if ``with_scaling`` is ``False``.
)");
// open3d.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([](std::shared_ptr<RobustKernel> kernel) {
return new TransformationEstimationPointToPlane(
std::move(kernel));
}),
"kernel"_a)
.def("__repr__",
[](const TransformationEstimationPointToPlane &te) {
return std::string("TransformationEstimationPointToPlane");
})
.def_readwrite("kernel",
&TransformationEstimationPointToPlane::kernel_,
"Robust Kernel used in the Optimization");
// open3d.registration.TransformationEstimationForColoredICP :
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,
std::shared_ptr<RobustKernel> kernel) {
return new TransformationEstimationForColoredICP(
lambda_geometric, std::move(kernel));
}),
"lambda_geometric"_a, "kernel"_a)
.def(py::init([](double lambda_geometric) {
return new TransformationEstimationForColoredICP(
lambda_geometric);
}),
"lambda_geometric"_a)
.def(py::init([](std::shared_ptr<RobustKernel> kernel) {
auto te = TransformationEstimationForColoredICP();
te.kernel_ = std::move(kernel);
return te;
}),
"kernel"_a)
.def("__repr__",
[](const TransformationEstimationForColoredICP &te) {
// This is missing kernel, but getting kernel name on C++
// is hard
return fmt::format(
"TransformationEstimationForColoredICP("
"lambda_geometric={})",
te.lambda_geometric_);
})
.def_readwrite(
"lambda_geometric",
&TransformationEstimationForColoredICP::lambda_geometric_,
"lambda_geometric")
.def_readwrite("kernel",
&TransformationEstimationForColoredICP::kernel_,
"Robust Kernel used in the Optimization");
// open3d.registration.TransformationEstimationForGeneralizedICP:
// TransformationEstimation
auto te_gicp = static_cast<
py::class_<TransformationEstimationForGeneralizedICP,
PyTransformationEstimation<
TransformationEstimationForGeneralizedICP>,
TransformationEstimation>>(
m_registration.attr("TransformationEstimationForGeneralizedICP"));
py::detail::bind_default_constructor<
TransformationEstimationForGeneralizedICP>(te_gicp);
py::detail::bind_copy_functions<TransformationEstimationForGeneralizedICP>(
te_gicp);
te_gicp.def(py::init([](double epsilon,
std::shared_ptr<RobustKernel> kernel) {
return new TransformationEstimationForGeneralizedICP(
epsilon, std::move(kernel));
}),
"epsilon"_a, "kernel"_a)
.def(py::init([](double epsilon) {
return new TransformationEstimationForGeneralizedICP(
epsilon);
}),
"epsilon"_a)
.def(py::init([](std::shared_ptr<RobustKernel> kernel) {
auto te = TransformationEstimationForGeneralizedICP();
te.kernel_ = std::move(kernel);
return te;
}),
"kernel"_a)
.def("__repr__",
[](const TransformationEstimationForGeneralizedICP &te) {
return fmt::format(
"TransformationEstimationForGeneralizedICP("
"epsilon={})",
te.epsilon_);
})
.def_readwrite("epsilon",
&TransformationEstimationForGeneralizedICP::epsilon_,
"epsilon")
.def_readwrite("kernel",
&TransformationEstimationForGeneralizedICP::kernel_,
"Robust Kernel used in the Optimization");
// open3d.registration.CorrespondenceChecker
auto cc = static_cast<
py::class_<CorrespondenceChecker,
PyCorrespondenceChecker<CorrespondenceChecker>>>(
m_registration.attr("CorrespondenceChecker"));
cc.def("Check", &CorrespondenceChecker::Check, "source"_a, "target"_a,
"corres"_a, "transformation"_a,
"Function to check if two points can be aligned. The two input "
"point clouds must have exact the same number of points.");
cc.def_readwrite(
"require_pointcloud_alignment_",
&CorrespondenceChecker::require_pointcloud_alignment_,
"Some checkers do not require point clouds to be aligned, e.g., "
"the edge length checker. Some checkers do, e.g., the distance "
"checker.");
docstring::ClassMethodDocInject(
m_registration, "CorrespondenceChecker", "Check",
{{"source", "Source point cloud."},
{"target", "Target point cloud."},
{"corres",
"Correspondence set between source and target point cloud."},
{"transformation", "The estimated transformation (inplace)."}});
// open3d.registration.CorrespondenceCheckerBasedOnEdgeLength:
// CorrespondenceChecker
auto cc_el = static_cast<py::class_<
CorrespondenceCheckerBasedOnEdgeLength,
PyCorrespondenceChecker<CorrespondenceCheckerBasedOnEdgeLength>,
CorrespondenceChecker>>(
m_registration.attr("CorrespondenceCheckerBasedOnEdgeLength"));
py::detail::bind_copy_functions<CorrespondenceCheckerBasedOnEdgeLength>(
cc_el);
cc_el.def(py::init([](double similarity_threshold) {
return new CorrespondenceCheckerBasedOnEdgeLength(
similarity_threshold);
}),
"similarity_threshold"_a = 0.9)
.def("__repr__",
[](const CorrespondenceCheckerBasedOnEdgeLength &c) {
return fmt::format(
""
"CorrespondenceCheckerBasedOnEdgeLength "
"with similarity_threshold={:f}",
c.similarity_threshold_);
})
.def_readwrite(
"similarity_threshold",
&CorrespondenceCheckerBasedOnEdgeLength::
similarity_threshold_,
R"(float value between 0 (loose) and 1 (strict): For the
check to be true,
:math:`||\text{edge}_{\text{source}}|| > \text{similarity_threshold} \times ||\text{edge}_{\text{target}}||` and
:math:`||\text{edge}_{\text{target}}|| > \text{similarity_threshold} \times ||\text{edge}_{\text{source}}||`
must hold true for all edges.)");
// open3d.registration.CorrespondenceCheckerBasedOnDistance:
// CorrespondenceChecker
auto cc_d = static_cast<py::class_<
CorrespondenceCheckerBasedOnDistance,
PyCorrespondenceChecker<CorrespondenceCheckerBasedOnDistance>,
CorrespondenceChecker>>(
m_registration.attr("CorrespondenceCheckerBasedOnDistance"));
py::detail::bind_copy_functions<CorrespondenceCheckerBasedOnDistance>(cc_d);
cc_d.def(py::init([](double distance_threshold) {
return new CorrespondenceCheckerBasedOnDistance(
distance_threshold);
}),
"distance_threshold"_a)
.def("__repr__",
[](const CorrespondenceCheckerBasedOnDistance &c) {
return fmt::format(
""
"CorrespondenceCheckerBasedOnDistance with "
"distance_threshold={:f}",
c.distance_threshold_);
})
.def_readwrite(
"distance_threshold",
&CorrespondenceCheckerBasedOnDistance::distance_threshold_,
"Distance threshold for the check.");
// open3d.registration.CorrespondenceCheckerBasedOnNormal:
// CorrespondenceChecker
auto cc_n = static_cast<py::class_<
CorrespondenceCheckerBasedOnNormal,
PyCorrespondenceChecker<CorrespondenceCheckerBasedOnNormal>,
CorrespondenceChecker>>(
m_registration.attr("CorrespondenceCheckerBasedOnNormal"));
py::detail::bind_copy_functions<CorrespondenceCheckerBasedOnNormal>(cc_n);
cc_n.def(py::init([](double normal_angle_threshold) {
return new CorrespondenceCheckerBasedOnNormal(
normal_angle_threshold);
}),
"normal_angle_threshold"_a)
.def("__repr__",
[](const CorrespondenceCheckerBasedOnNormal &c) {
return fmt::format(
""
"CorrespondenceCheckerBasedOnNormal with "
"normal_threshold={:f}",
c.normal_angle_threshold_);
})
.def_readwrite("normal_angle_threshold",
&CorrespondenceCheckerBasedOnNormal::
normal_angle_threshold_,
"Radian value for angle threshold.");
// open3d.registration.FastGlobalRegistrationOption:
auto fgr_option = static_cast<py::class_<FastGlobalRegistrationOption>>(
m_registration.attr("FastGlobalRegistrationOption"));
py::detail::bind_copy_functions<FastGlobalRegistrationOption>(fgr_option);
fgr_option
.def(py::init([](double division_factor, bool use_absolute_scale,
bool decrease_mu,
double maximum_correspondence_distance,
int iteration_number, double tuple_scale,
int maximum_tuple_count, bool tuple_test) {
return new FastGlobalRegistrationOption(
division_factor, use_absolute_scale, decrease_mu,
maximum_correspondence_distance, iteration_number,
tuple_scale, maximum_tuple_count, tuple_test);
}),
"division_factor"_a = 1.4, "use_absolute_scale"_a = false,
"decrease_mu"_a = false,
"maximum_correspondence_distance"_a = 0.025,
"iteration_number"_a = 64, "tuple_scale"_a = 0.95,
"maximum_tuple_count"_a = 1000, "tuple_test"_a = true)
.def_readwrite(
"division_factor",
&FastGlobalRegistrationOption::division_factor_,
"float: Division factor used for graduated non-convexity.")
.def_readwrite(
"use_absolute_scale",
&FastGlobalRegistrationOption::use_absolute_scale_,
"bool: Measure distance in absolute scale (1) or in scale "
"relative to the diameter of the model (0).")
.def_readwrite("decrease_mu",
&FastGlobalRegistrationOption::decrease_mu_,
"bool: Set to ``True`` to decrease scale mu by "
"``division_factor`` for graduated non-convexity.")
.def_readwrite("maximum_correspondence_distance",
&FastGlobalRegistrationOption::
maximum_correspondence_distance_,
"float: Maximum correspondence distance.")
.def_readwrite("iteration_number",
&FastGlobalRegistrationOption::iteration_number_,
"int: Maximum number of iterations.")
.def_readwrite(
"tuple_scale", &FastGlobalRegistrationOption::tuple_scale_,
"float: Similarity measure used for tuples of feature "
"points.")
.def_readwrite("maximum_tuple_count",
&FastGlobalRegistrationOption::maximum_tuple_count_,
"float: Maximum tuple numbers.")
.def_readwrite(
"tuple_test", &FastGlobalRegistrationOption::tuple_test_,
"bool: Set to `true` to perform geometric compatibility "
"tests on initial set of correspondences.")
.def("__repr__", [](const FastGlobalRegistrationOption &c) {
return fmt::format(
"FastGlobalRegistrationOption("
"\ndivision_factor={},"
"\nuse_absolute_scale={},"
"\ndecrease_mu={},"
"\nmaximum_correspondence_distance={},"
"\niteration_number={},"
"\ntuple_scale={},"
"\nmaximum_tuple_count={},"
"\ntuple_test={},"
"\n)",
c.division_factor_, c.use_absolute_scale_,
c.decrease_mu_, c.maximum_correspondence_distance_,
c.iteration_number_, c.tuple_scale_,
c.maximum_tuple_count_, c.tuple_test_);
});
// open3d.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 numpy array: The estimated "
"transformation matrix.")
.def_readwrite(
"correspondence_set",
&RegistrationResult::correspondence_set_,
"``n x 2`` int numpy array: Correspondence set between "
"source and target point cloud.")
.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("__repr__", [](const RegistrationResult &rr) {
return fmt::format(
"RegistrationResult with "
"fitness={:e}"
", inlier_rmse={:e}"
", and correspondence_set size of {:d}"
"\nAccess transformation to get result.",
rr.fitness_, rr.inlier_rmse_,
rr.correspondence_set_.size());
});
// Registration functions have similar arguments, sharing arg docstrings
static const std::unordered_map<std::string, std::string>
map_shared_argument_docstrings = {
{"checkers",
"Vector of Checker class to check if two point "
"clouds can be aligned. One of "
"(``CorrespondenceCheckerBasedOnEdgeLength``, "
"``CorrespondenceCheckerBasedOnDistance``, "
"``CorrespondenceCheckerBasedOnNormal``)"},
{"confidence",
"Desired probability of success for RANSAC. Used for "
"estimating early termination by k = log(1 - "
"confidence)/log(1 - inlier_ratio^{ransac_n}."},
{"corres",
"o3d.utility.Vector2iVector that stores indices of "
"corresponding point or feature arrays."},
{"criteria", "Convergence criteria"},
{"estimation_method",
"Estimation method. One of "
"(``TransformationEstimationPointToPoint``, "
"``TransformationEstimationPointToPlane``, "
"``TransformationEstimationForGeneralizedICP``, "
"``TransformationEstimationForColoredICP``)"},
{"init", "Initial transformation estimation"},
{"lambda_geometric", "lambda_geometric value"},
{"epsilon", "epsilon value"},
{"kernel", "Robust Kernel used in the Optimization"},
{"max_correspondence_distance",
"Maximum correspondence points-pair distance."},
{"mutual_filter",
"Enables mutual filter such that the correspondence of "
"the "
"source point's correspondence is itself."},
{"option", "Registration option"},
{"ransac_n",
"Fit ransac with ``ransac_n`` correspondences"},
{"source_feature", "Source point cloud feature."},
{"source", "The source point cloud."},
{"target_feature", "Target point cloud feature."},
{"target", "The target point cloud."},
{"transformation",
"The 4x4 transformation matrix to transform ``source`` to "
"``target``"}};
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 = Eigen::Matrix4d::Identity());
docstring::FunctionDocInject(m_registration, "evaluate_registration",
map_shared_argument_docstrings);
m_registration.def(
"registration_icp", &RegistrationICP,
py::call_guard<py::gil_scoped_release>(),
"Function for ICP registration", "source"_a, "target"_a,
"max_correspondence_distance"_a,
"init"_a = Eigen::Matrix4d::Identity(),
"estimation_method"_a = TransformationEstimationPointToPoint(false),
"criteria"_a = ICPConvergenceCriteria());
docstring::FunctionDocInject(m_registration, "registration_icp",
map_shared_argument_docstrings);
m_registration.def("registration_colored_icp", &RegistrationColoredICP,
py::call_guard<py::gil_scoped_release>(),
"Function for Colored ICP registration", "source"_a,
"target"_a, "max_correspondence_distance"_a,
"init"_a = Eigen::Matrix4d::Identity(),
"estimation_method"_a =
TransformationEstimationForColoredICP(0.968),
"criteria"_a = ICPConvergenceCriteria());
docstring::FunctionDocInject(m_registration, "registration_colored_icp",
map_shared_argument_docstrings);
m_registration.def("registration_generalized_icp",
&RegistrationGeneralizedICP,
py::call_guard<py::gil_scoped_release>(),
"Function for Generalized ICP registration", "source"_a,
"target"_a, "max_correspondence_distance"_a,
"init"_a = Eigen::Matrix4d::Identity(),
"estimation_method"_a =
TransformationEstimationForGeneralizedICP(1e-3),
"criteria"_a = ICPConvergenceCriteria());
docstring::FunctionDocInject(m_registration, "registration_generalized_icp",
map_shared_argument_docstrings);
m_registration.def(
"registration_ransac_based_on_correspondence",
&RegistrationRANSACBasedOnCorrespondence,
py::call_guard<py::gil_scoped_release>(),
"Function for global RANSAC registration based on a set of "
"correspondences",
"source"_a, "target"_a, "corres"_a, "max_correspondence_distance"_a,
"estimation_method"_a = TransformationEstimationPointToPoint(false),
"ransac_n"_a = 3,
"checkers"_a = std::vector<
std::reference_wrapper<const CorrespondenceChecker>>(),
"criteria"_a = RANSACConvergenceCriteria(100000, 0.999));
docstring::FunctionDocInject(m_registration,
"registration_ransac_based_on_correspondence",
map_shared_argument_docstrings);
m_registration.def(
"registration_ransac_based_on_feature_matching",
&RegistrationRANSACBasedOnFeatureMatching,
py::call_guard<py::gil_scoped_release>(),
"Function for global RANSAC registration based on feature matching",
"source"_a, "target"_a, "source_feature"_a, "target_feature"_a,
"mutual_filter"_a, "max_correspondence_distance"_a,
"estimation_method"_a = TransformationEstimationPointToPoint(false),
"ransac_n"_a = 3,
"checkers"_a = std::vector<
std::reference_wrapper<const CorrespondenceChecker>>(),
"criteria"_a = RANSACConvergenceCriteria(100000, 0.999));
docstring::FunctionDocInject(
m_registration, "registration_ransac_based_on_feature_matching",
map_shared_argument_docstrings);
m_registration.def(
"registration_fgr_based_on_correspondence",
&FastGlobalRegistrationBasedOnCorrespondence,
py::call_guard<py::gil_scoped_release>(),
"Function for fast global registration based on a set of "
"correspondences",
"source"_a, "target"_a, "corres"_a,
"option"_a = FastGlobalRegistrationOption());
docstring::FunctionDocInject(m_registration,
"registration_fgr_based_on_correspondence",
map_shared_argument_docstrings);
m_registration.def(
"registration_fgr_based_on_feature_matching",
&FastGlobalRegistrationBasedOnFeatureMatching,
py::call_guard<py::gil_scoped_release>(),
"Function for fast global registration based on feature matching",
"source"_a, "target"_a, "source_feature"_a, "target_feature"_a,
"option"_a = FastGlobalRegistrationOption());
docstring::FunctionDocInject(m_registration,
"registration_fgr_based_on_feature_matching",
map_shared_argument_docstrings);
m_registration.def(
"get_information_matrix_from_point_clouds",
&GetInformationMatrixFromPointClouds,
py::call_guard<py::gil_scoped_release>(),
"Function for computing information matrix from transformation "
"matrix",
"source"_a, "target"_a, "max_correspondence_distance"_a,
"transformation"_a);
docstring::FunctionDocInject(m_registration,
"get_information_matrix_from_point_clouds",
map_shared_argument_docstrings);
pybind_feature_definitions(m_registration);
pybind_global_optimization_definitions(m_registration);
pybind_robust_kernels_definitions(m_registration);
}
} // namespace registration
} // namespace pipelines
} // namespace open3d
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