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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// The MIT License (MIT)
//
// Copyright (c) 2018-2021 www.open3d.org
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
// IN THE SOFTWARE.
// ----------------------------------------------------------------------------
#include "pybind/t/pipelines/odometry/odometry.h"
#include "open3d/t/pipelines/odometry/RGBDOdometry.h"
#include "pybind/docstring.h"
namespace open3d {
namespace t {
namespace pipelines {
namespace odometry {
void pybind_odometry_classes(py::module &m) {
py::enum_<Method>(m, "Method", "Tensor odometry estimation method.")
.value("PointToPlane", Method::PointToPlane)
.value("Intensity", Method::Intensity)
.value("Hybrid", Method::Hybrid)
.export_values();
// open3d.t.pipelines.odometry.OdometryConvergenceCriteria
py::class_<OdometryConvergenceCriteria> odometry_convergence_criteria(
m, "OdometryConvergenceCriteria",
"Convergence criteria of odometry. "
"Odometry 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::detail::bind_copy_functions<OdometryConvergenceCriteria>(
odometry_convergence_criteria);
odometry_convergence_criteria
.def(py::init<int, double, double>(), "max_iteration"_a,
"relative_rmse"_a = 1e-6, "relative_fitness"_a = 1e-6)
.def_readwrite("max_iteration",
&OdometryConvergenceCriteria::max_iteration_,
"Maximum iteration before iteration stops.")
.def_readwrite(
"relative_rmse",
&OdometryConvergenceCriteria::relative_rmse_,
"If relative change (difference) of inliner RMSE score is "
"lower than ``relative_rmse``, the iteration stops.")
.def_readwrite(
"relative_fitness",
&OdometryConvergenceCriteria::relative_fitness_,
"If relative change (difference) of fitness score is lower "
"than ``relative_fitness``, the iteration stops.")
.def("__repr__", [](const OdometryConvergenceCriteria &c) {
return fmt::format(
"OdometryConvergenceCriteria[max_iteration={:d}, "
"relative_rmse={:e}, relative_fitness={:e}].",
c.max_iteration_, c.relative_rmse_,
c.relative_fitness_);
});
// open3d.t.pipelines.odometry.OdometryResult
py::class_<OdometryResult> odometry_result(m, "OdometryResult",
"Odometry results.");
py::detail::bind_copy_functions<OdometryResult>(odometry_result);
odometry_result
.def(py::init<core::Tensor, double, double>(),
"transformation"_a = core::Tensor::Eye(4, core::Float64,
core::Device("CPU:0")),
"inlier_rmse"_a = 0.0, "fitness"_a = 0.0)
.def_readwrite("transformation", &OdometryResult::transformation_,
"``4 x 4`` float64 tensor on CPU: The estimated "
"transformation matrix.")
.def_readwrite("inlier_rmse", &OdometryResult::inlier_rmse_,
"float: RMSE of all inlier correspondences. Lower "
"is better.")
.def_readwrite(
"fitness", &OdometryResult::fitness_,
"float: The overlapping area (# of inlier correspondences "
"/ # of points in target). Higher is better.")
.def("__repr__", [](const OdometryResult &odom_result) {
return fmt::format(
"OdometryResult[fitness={:e}, inlier_rmse={:e}]."
"\nAccess transformation to get result.",
odom_result.fitness_, odom_result.inlier_rmse_);
});
// open3d.t.pipelines.odometry.OdometryLossParams
py::class_<OdometryLossParams> odometry_loss_params(
m, "OdometryLossParams", "Odometry loss parameters.");
py::detail::bind_copy_functions<OdometryLossParams>(odometry_loss_params);
odometry_loss_params
.def(py::init<double, double, double>(),
"depth_outlier_trunc"_a = 0.07, "depth_huber_delta"_a = 0.05,
"intensity_huber_delta"_a = 0.1)
.def_readwrite("depth_outlier_trunc",
&OdometryLossParams::depth_outlier_trunc_,
"float: Depth difference threshold used to filter "
"projective associations.")
.def_readwrite("depth_huber_delta",
&OdometryLossParams::depth_huber_delta_,
"float: Huber norm parameter used in depth loss.")
.def_readwrite(
"intensity_huber_delta",
&OdometryLossParams::intensity_huber_delta_,
"float: Huber norm parameter used in intensity loss.")
.def("__repr__", [](const OdometryLossParams &olp) {
return fmt::format(
"OdometryLossParams[depth_outlier_trunc={:e}, "
"depth_huber_delta={:e}, intensity_huber_delta={:e}].",
olp.depth_outlier_trunc_, olp.depth_huber_delta_,
olp.intensity_huber_delta_);
});
}
// Odometry functions have similar arguments, sharing arg docstrings.
static const std::unordered_map<std::string, std::string>
map_shared_argument_docstrings = {
{"criteria", "Odometry convergence criteria."},
{"criteria_list", "List of Odometry convergence criteria."},
{"depth_outlier_trunc",
"Depth difference threshold used to filter projective "
"associations."},
{"depth_huber_delta",
"Huber norm parameter used in depth loss."},
{"depth_scale",
"Converts depth pixel values to meters by dividing the scale "
"factor."},
{"init_source_to_target",
"(4, 4) initial transformation matrix from source to target."},
{"intrinsics", "(3, 3) intrinsic matrix for projection."},
{"intensity_huber_delta",
"Huber norm parameter used in intensity loss."},
{"method",
"Estimation method used to apply RGBD odometry. "
"One of (``PointToPlane``, ``Intensity``, ``Hybrid``)"},
{"params", "Odometry loss parameters."},
{"source", "The source RGBD image."},
{"source_depth",
"(row, col, channel = 1) Float32 source depth image obtained "
"by PreprocessDepth before calling this function."},
{"source_intensity",
"(row, col, channel = 1) Float32 source intensity image "
"obtained by RGBToGray before calling this function"},
{"source_vertex_map",
"(row, col, channel = 3) Float32 source vertex image obtained "
"by CreateVertexMap before calling this function."},
{"target", "The target RGBD image."},
{"target_depth",
"(row, col, channel = 1) Float32 target depth image obtained "
"by PreprocessDepth before calling this function."},
{"target_depth_dx",
"(row, col, channel = 1) Float32 target depth gradient image "
"along x-axis obtained by FilterSobel before calling this "
"function."},
{"target_depth_dy",
"(row, col, channel = 1) Float32 target depth gradient image "
"along y-axis obtained by FilterSobel before calling this "
"function."},
{"target_intensity",
"(row, col, channel = 1) Float32 target intensity image "
"obtained by RGBToGray before calling this function"},
{"target_intensity_dx",
"(row, col, channel = 1) Float32 target intensity gradient "
"image along x-axis obtained by FilterSobel before calling "
"this function."},
{"target_intensity_dy",
"(row, col, channel = 1) Float32 target intensity gradient "
"image along y-axis obtained by FilterSobel before calling "
"this function."},
{"target_normal_map",
"(row, col, channel = 3) Float32 target normal image obtained "
"by CreateNormalMap before calling this function."},
{"target_vertex_map",
"(row, col, channel = 3) Float32 target vertex image obtained "
"by CreateVertexMap before calling this function."}};
void pybind_odometry_methods(py::module &m) {
m.def("rgbd_odometry_multi_scale", &RGBDOdometryMultiScale,
py::call_guard<py::gil_scoped_release>(),
"Function for Multi Scale RGBD odometry.", "source"_a, "target"_a,
"intrinsics"_a,
"init_source_to_target"_a =
core::Tensor::Eye(4, core::Float64, core::Device("CPU:0")),
"depth_scale"_a = 1000.0f, "depth_max"_a = 3.0f,
"criteria_list"_a =
std::vector<OdometryConvergenceCriteria>({10, 5, 3}),
"method"_a = Method::Hybrid, "params"_a = OdometryLossParams());
docstring::FunctionDocInject(m, "rgbd_odometry_multi_scale",
map_shared_argument_docstrings);
m.def("compute_odometry_result_point_to_plane",
&ComputeOdometryResultPointToPlane,
py::call_guard<py::gil_scoped_release>(),
R"(Estimates the OdometryResult (4x4 rigid transformation T from
source to target, with inlier rmse and fitness). Performs one
iteration of RGBD odometry using
Loss function: :math:`[(V_p - V_q)^T N_p]^2`
where,
:math:`V_p` denotes the vertex at pixel p in the source,
:math:`V_q` denotes the vertex at pixel q in the target.
:math:`N_p` denotes the normal at pixel p in the source.
q is obtained by transforming p with init_source_to_target then
projecting with intrinsics.
Reference: KinectFusion, ISMAR 2011.)",
"source_vertex_map"_a, "target_vertex_map"_a, "target_normal_map"_a,
"intrinsics"_a, "init_source_to_target"_a, "depth_outlier_trunc"_a,
"depth_huber_delta"_a);
docstring::FunctionDocInject(m, "compute_odometry_result_point_to_plane",
map_shared_argument_docstrings);
m.def("compute_odometry_result_intensity", &ComputeOdometryResultIntensity,
py::call_guard<py::gil_scoped_release>(),
R"(Estimates the OdometryResult (4x4 rigid transformation T from
source to target, with inlier rmse and fitness). Performs one
iteration of RGBD odometry using
Loss function: :math:`(I_p - I_q)^2`
where,
:math:`I_p` denotes the intensity at pixel p in the source,
:math:`I_q` denotes the intensity at pixel q in the target.
q is obtained by transforming p with init_source_to_target then
projecting with intrinsics.
Reference:
Real-time visual odometry from dense RGB-D images,
ICCV Workshops, 2017.)",
"source_depth"_a, "target_depth"_a, "source_intensity"_a,
"target_intensity"_a, "target_intensity_dx"_a,
"target_intensity_dy"_a, "source_vertex_map"_a, "intrinsics"_a,
"init_source_to_target"_a, "depth_outlier_trunc"_a,
"intensity_huber_delta"_a);
docstring::FunctionDocInject(m, "compute_odometry_result_intensity",
map_shared_argument_docstrings);
m.def("compute_odometry_result_hybrid", &ComputeOdometryResultHybrid,
py::call_guard<py::gil_scoped_release>(),
R"(Estimates the OdometryResult (4x4 rigid transformation T from
source to target, with inlier rmse and fitness). Performs one
iteration of RGBD odometry using
Loss function: :math:`(I_p - I_q)^2 + \lambda(D_p - (D_q)')^2`
where,
:math:`I_p` denotes the intensity at pixel p in the source,
:math:`I_q` denotes the intensity at pixel q in the target.
:math:`D_p` denotes the depth pixel p in the source,
:math:`D_q` denotes the depth pixel q in the target.
q is obtained by transforming p with init_source_to_target then
projecting with intrinsics.
Reference: J. Park, Q.Y. Zhou, and V. Koltun,
Colored Point Cloud Registration Revisited, ICCV, 2017.)",
"source_depth"_a, "target_depth"_a, "source_intensity"_a,
"target_intensity"_a, "target_depth_dx"_a, "target_depth_dy"_a,
"target_intensity_dx"_a, "target_intensity_dy"_a,
"source_vertex_map"_a, "intrinsics"_a, "init_source_to_target"_a,
"depth_outlier_trunc"_a, "depth_huber_delta"_a,
"intensity_huber_delta"_a);
docstring::FunctionDocInject(m, "compute_odometry_result_hybrid",
map_shared_argument_docstrings);
}
void pybind_odometry(py::module &m) {
py::module m_submodule =
m.def_submodule("odometry", "Tensor odometry pipeline.");
pybind_odometry_classes(m_submodule);
pybind_odometry_methods(m_submodule);
}
} // namespace odometry
} // namespace pipelines
} // namespace t
} // namespace open3d
|
