1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
|
// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include "pybind/pipelines/integration/integration.h"
#include "open3d/geometry/VoxelGrid.h"
#include "open3d/pipelines/integration/ScalableTSDFVolume.h"
#include "open3d/pipelines/integration/TSDFVolume.h"
#include "open3d/pipelines/integration/UniformTSDFVolume.h"
#include "pybind/docstring.h"
namespace open3d {
namespace pipelines {
namespace integration {
template <class TSDFVolumeBase = TSDFVolume>
class PyTSDFVolume : public TSDFVolumeBase {
public:
using TSDFVolumeBase::TSDFVolumeBase;
void Reset() override { PYBIND11_OVERLOAD_PURE(void, TSDFVolumeBase, ); }
void Integrate(const geometry::RGBDImage &image,
const camera::PinholeCameraIntrinsic &intrinsic,
const Eigen::Matrix4d &extrinsic) override {
PYBIND11_OVERLOAD_PURE(void, TSDFVolumeBase, image, intrinsic,
extrinsic);
}
std::shared_ptr<geometry::PointCloud> ExtractPointCloud() override {
PYBIND11_OVERLOAD_PURE(std::shared_ptr<geometry::PointCloud>,
TSDFVolumeBase, );
}
std::shared_ptr<geometry::TriangleMesh> ExtractTriangleMesh() override {
PYBIND11_OVERLOAD_PURE(std::shared_ptr<geometry::TriangleMesh>,
TSDFVolumeBase, );
}
};
void pybind_integration_declarations(py::module &m) {
py::module m_integration =
m.def_submodule("integration", "Integration pipeline.");
// open3d.integration.TSDFVolumeColorType
py::enum_<TSDFVolumeColorType> tsdf_volume_color_type(
m_integration, "TSDFVolumeColorType", py::arithmetic());
tsdf_volume_color_type.value("NoColor", TSDFVolumeColorType::NoColor)
.value("RGB8", TSDFVolumeColorType::RGB8)
.value("Gray32", TSDFVolumeColorType::Gray32)
.export_values();
// Trick to write docs without listing the members in the enum class again.
tsdf_volume_color_type.attr("__doc__") = docstring::static_property(
py::cpp_function([](py::handle arg) -> std::string {
return "Enum class for TSDFVolumeColorType.";
}),
py::none(), py::none(), "");
py::class_<TSDFVolume, PyTSDFVolume<TSDFVolume>> tsdfvolume(
m_integration, "TSDFVolume", R"(Base class of the Truncated
Signed Distance Function (TSDF) volume This volume is usually used to integrate
surface data (e.g., a series of RGB-D images) into a Mesh or PointCloud. The
basic technique is presented in the following paper:
A volumetric method for building complex models from range images
B. Curless and M. Levoy
In SIGGRAPH, 1996)");
py::class_<UniformTSDFVolume, PyTSDFVolume<UniformTSDFVolume>, TSDFVolume>
uniform_tsdfvolume(
m_integration, "UniformTSDFVolume",
"UniformTSDFVolume implements the classic TSDF "
"volume with uniform voxel grid (Curless and Levoy 1996).");
py::class_<ScalableTSDFVolume, PyTSDFVolume<ScalableTSDFVolume>, TSDFVolume>
scalable_tsdfvolume(m_integration, "ScalableTSDFVolume", R"(The
ScalableTSDFVolume implements a more memory efficient data structure for
volumetric integration.
This implementation is based on the following repository:
https://github.com/qianyizh/ElasticReconstruction/tree/master/Integrate
An observed depth pixel gives two types of information: (a) an approximation
of the nearby surface, and (b) empty space from the camera to the surface.
They induce two core concepts of volumetric integration: weighted average of
a truncated signed distance function (TSDF), and carving. The weighted
average of TSDF is great in addressing the Gaussian noise along surface
normal and producing a smooth surface output. The carving is great in
removing outlier structures like floating noise pixels and bumps along
structure edges.
Ref: Dense Scene Reconstruction with Points of Interest
Q.-Y. Zhou and V. Koltun
In SIGGRAPH, 2013)");
}
void pybind_integration_definitions(py::module &m) {
auto m_integration = static_cast<py::module>(m.attr("integration"));
// open3d.integration.TSDFVolume
auto tsdfvolume =
static_cast<py::class_<TSDFVolume, PyTSDFVolume<TSDFVolume>>>(
m_integration.attr("TSDFVolume"));
tsdfvolume
.def("reset", &TSDFVolume::Reset,
"Function to reset the TSDFVolume")
.def("integrate", &TSDFVolume::Integrate,
"Function to integrate an RGB-D image into the volume",
"image"_a, "intrinsic"_a, "extrinsic"_a)
.def("extract_point_cloud", &TSDFVolume::ExtractPointCloud,
"Function to extract a point cloud with normals")
.def("extract_triangle_mesh", &TSDFVolume::ExtractTriangleMesh,
"Function to extract a triangle mesh")
.def_readwrite("voxel_length", &TSDFVolume::voxel_length_,
"float: Length of the voxel in meters.")
.def_readwrite("sdf_trunc", &TSDFVolume::sdf_trunc_,
"float: Truncation value for signed distance "
"function (SDF).")
.def_readwrite("color_type", &TSDFVolume::color_type_,
"integration.TSDFVolumeColorType: Color type of the "
"TSDF volume.");
docstring::ClassMethodDocInject(m_integration, "TSDFVolume",
"extract_point_cloud");
docstring::ClassMethodDocInject(m_integration, "TSDFVolume",
"extract_triangle_mesh");
docstring::ClassMethodDocInject(
m_integration, "TSDFVolume", "integrate",
{{"image", "RGBD image."},
{"intrinsic", "Pinhole camera intrinsic parameters."},
{"extrinsic", "Extrinsic parameters."}});
docstring::ClassMethodDocInject(m_integration, "TSDFVolume", "reset");
// open3d.integration.UniformTSDFVolume: open3d.integration.TSDFVolume
auto uniform_tsdfvolume = static_cast<py::class_<
UniformTSDFVolume, PyTSDFVolume<UniformTSDFVolume>, TSDFVolume>>(
m_integration.attr("UniformTSDFVolume"));
py::detail::bind_copy_functions<UniformTSDFVolume>(uniform_tsdfvolume);
uniform_tsdfvolume
.def(py::init([](double length, int resolution, double sdf_trunc,
TSDFVolumeColorType color_type) {
return new UniformTSDFVolume(length, resolution, sdf_trunc,
color_type);
}),
"length"_a, "resolution"_a, "sdf_trunc"_a, "color_type"_a)
.def(py::init([](double length, int resolution, double sdf_trunc,
TSDFVolumeColorType color_type,
Eigen::Vector3d origin) {
return new UniformTSDFVolume(length, resolution, sdf_trunc,
color_type, origin);
}),
"length"_a, "resolution"_a, "sdf_trunc"_a, "color_type"_a,
"origin"_a)
.def("__repr__",
[](const UniformTSDFVolume &vol) {
return std::string(
""
"UniformTSDFVolume ") +
(vol.color_type_ == TSDFVolumeColorType::NoColor
? std::string("without color.")
: std::string("with color."));
}) // todo: extend
.def("extract_voxel_point_cloud",
&UniformTSDFVolume::ExtractVoxelPointCloud,
"Debug function to extract the voxel data into a point cloud.")
.def("extract_voxel_grid", &UniformTSDFVolume::ExtractVoxelGrid,
"Debug function to extract the voxel data VoxelGrid.")
.def("extract_volume_tsdf", &UniformTSDFVolume::ExtractVolumeTSDF,
"Debug function to extract the volume TSDF data.")
.def("extract_volume_color", &UniformTSDFVolume::ExtractVolumeColor,
"Debug function to extract the volume color data.")
.def("inject_volume_tsdf", &UniformTSDFVolume::InjectVolumeTSDF,
"Debug function to inject the voxel TSDF data.", "tsdf"_a)
.def("inject_volume_color", &UniformTSDFVolume::InjectVolumeColor,
"Debug function to inject the voxel Color data.", "color"_a)
.def_readwrite("length", &UniformTSDFVolume::length_,
"Total length, where ``voxel_length = length / "
"resolution``.")
.def_readwrite("resolution", &UniformTSDFVolume::resolution_,
"Resolution over the total length, where "
"``voxel_length = length / resolution``");
docstring::ClassMethodDocInject(m_integration, "UniformTSDFVolume",
"extract_voxel_point_cloud");
// open3d.integration.ScalableTSDFVolume: open3d.integration.TSDFVolume
auto scalable_tsdfvolume = static_cast<py::class_<
ScalableTSDFVolume, PyTSDFVolume<ScalableTSDFVolume>, TSDFVolume>>(
m_integration.attr("ScalableTSDFVolume"));
py::detail::bind_copy_functions<ScalableTSDFVolume>(scalable_tsdfvolume);
scalable_tsdfvolume
.def(py::init([](double voxel_length, double sdf_trunc,
TSDFVolumeColorType color_type,
int volume_unit_resolution,
int depth_sampling_stride) {
return new ScalableTSDFVolume(
voxel_length, sdf_trunc, color_type,
volume_unit_resolution, depth_sampling_stride);
}),
"voxel_length"_a, "sdf_trunc"_a, "color_type"_a,
"volume_unit_resolution"_a = 16, "depth_sampling_stride"_a = 4)
.def("__repr__",
[](const ScalableTSDFVolume &vol) {
return std::string(
""
"ScalableTSDFVolume ") +
(vol.color_type_ == TSDFVolumeColorType::NoColor
? std::string("without color.")
: std::string("with color."));
})
.def("extract_voxel_point_cloud",
&ScalableTSDFVolume::ExtractVoxelPointCloud,
"Debug function to extract the voxel data into a point "
"cloud.");
docstring::ClassMethodDocInject(m_integration, "ScalableTSDFVolume",
"extract_voxel_point_cloud");
}
} // namespace integration
} // namespace pipelines
} // namespace open3d
|
