Make fragments
The first step of the scene reconstruction system is to create fragments from short RGBD sequences.
Input arguments
The script runs with python run_system.py [config] --make
. In [config]
,
["path_dataset"]
should have subfolders image
and depth
to store the
color images and depth images respectively. We assume the color images and the
depth images are synchronized and registered. In [config]
, the optional
argument ["path_intrinsic"]
specifies the path to a json file that stores
the camera intrinsic matrix (See
/tutorial/pipelines/rgbd_odometry.ipynb#read-camera-intrinsic for
details). If it is not given, the PrimeSense factory setting is used instead.
Register RGBD image pairs
46# examples/python/reconstruction_system/make_fragments.py
47
48def register_one_rgbd_pair(s, t, color_files, depth_files, intrinsic,
49 with_opencv, config):
50 source_rgbd_image = read_rgbd_image(color_files[s], depth_files[s], True,
51 config)
52 target_rgbd_image = read_rgbd_image(color_files[t], depth_files[t], True,
53 config)
54
55 option = o3d.pipelines.odometry.OdometryOption()
56 option.depth_diff_max = config["depth_diff_max"]
57 if abs(s - t) != 1:
58 if with_opencv:
59 success_5pt, odo_init = pose_estimation(source_rgbd_image,
60 target_rgbd_image,
61 intrinsic, False)
62 if success_5pt:
63 [success, trans, info
64 ] = o3d.pipelines.odometry.compute_rgbd_odometry(
65 source_rgbd_image, target_rgbd_image, intrinsic, odo_init,
66 o3d.pipelines.odometry.RGBDOdometryJacobianFromHybridTerm(),
67 option)
68 return [success, trans, info]
69 return [False, np.identity(4), np.identity(6)]
70 else:
71 odo_init = np.identity(4)
72 [success, trans, info] = o3d.pipelines.odometry.compute_rgbd_odometry(
73 source_rgbd_image, target_rgbd_image, intrinsic, odo_init,
74 o3d.pipelines.odometry.RGBDOdometryJacobianFromHybridTerm(), option)
75 return [success, trans, info]
76
The function reads a pair of RGBD images and registers the source_rgbd_image
to the target_rgbd_image
. The Open3D function compute_rgbd_odometry
is
called to align the RGBD images. For adjacent RGBD images, an identity matrix is
used as the initialization. For non-adjacent RGBD images, wide baseline matching
is used as the initialization. In particular, the function pose_estimation
computes OpenCV ORB feature to match sparse features over wide baseline images,
then performs 5-point RANSAC to estimate a rough alignment, which is used as
the initialization of compute_rgbd_odometry
.
Multiway registration
76# examples/python/reconstruction_system/make_fragments.py
77
78def make_posegraph_for_fragment(path_dataset, sid, eid, color_files,
79 depth_files, fragment_id, n_fragments,
80 intrinsic, with_opencv, config):
81 o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Error)
82 pose_graph = o3d.pipelines.registration.PoseGraph()
83 trans_odometry = np.identity(4)
84 pose_graph.nodes.append(
85 o3d.pipelines.registration.PoseGraphNode(trans_odometry))
86 for s in range(sid, eid):
87 for t in range(s + 1, eid):
88 # odometry
89 if t == s + 1:
90 print(
91 "Fragment %03d / %03d :: RGBD matching between frame : %d and %d"
92 % (fragment_id, n_fragments - 1, s, t))
93 [success, trans,
94 info] = register_one_rgbd_pair(s, t, color_files, depth_files,
95 intrinsic, with_opencv, config)
96 trans_odometry = np.dot(trans, trans_odometry)
97 trans_odometry_inv = np.linalg.inv(trans_odometry)
98 pose_graph.nodes.append(
99 o3d.pipelines.registration.PoseGraphNode(
100 trans_odometry_inv))
101 pose_graph.edges.append(
102 o3d.pipelines.registration.PoseGraphEdge(s - sid,
103 t - sid,
104 trans,
105 info,
106 uncertain=False))
107
108 # keyframe loop closure
109 if s % config['n_keyframes_per_n_frame'] == 0 \
110 and t % config['n_keyframes_per_n_frame'] == 0:
111 print(
112 "Fragment %03d / %03d :: RGBD matching between frame : %d and %d"
113 % (fragment_id, n_fragments - 1, s, t))
114 [success, trans,
115 info] = register_one_rgbd_pair(s, t, color_files, depth_files,
116 intrinsic, with_opencv, config)
117 if success:
118 pose_graph.edges.append(
119 o3d.pipelines.registration.PoseGraphEdge(
120 s - sid, t - sid, trans, info, uncertain=True))
121 o3d.io.write_pose_graph(
122 join(path_dataset, config["template_fragment_posegraph"] % fragment_id),
123 pose_graph)
This script uses the technique demonstrated in
/tutorial/pipelines/multiway_registration.ipynb. The function
make_posegraph_for_fragment
builds a pose graph for multiway registration of
all RGBD images in this sequence. Each graph node represents an RGBD image and
its pose which transforms the geometry to the global fragment space.
For efficiency, only key frames are used.
Once a pose graph is created, multiway registration is performed by calling the
function optimize_posegraph_for_fragment
.
51# examples/python/reconstruction_system/optimize_posegraph.py
52
53def optimize_posegraph_for_fragment(path_dataset, fragment_id, config):
54 pose_graph_name = join(path_dataset,
55 config["template_fragment_posegraph"] % fragment_id)
56 pose_graph_optimized_name = join(
57 path_dataset,
58 config["template_fragment_posegraph_optimized"] % fragment_id)
59 run_posegraph_optimization(pose_graph_name, pose_graph_optimized_name,
60 max_correspondence_distance = config["depth_diff_max"],
61 preference_loop_closure = \
62 config["preference_loop_closure_odometry"])
63
This function calls global_optimization
to estimate poses of the RGBD images.
Make a fragment
124# examples/python/reconstruction_system/make_fragments.py
125
126def integrate_rgb_frames_for_fragment(color_files, depth_files, fragment_id,
127 n_fragments, pose_graph_name, intrinsic,
128 config):
129 pose_graph = o3d.io.read_pose_graph(pose_graph_name)
130 volume = o3d.pipelines.integration.ScalableTSDFVolume(
131 voxel_length=config["tsdf_cubic_size"] / 512.0,
132 sdf_trunc=0.04,
133 color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8)
134 for i in range(len(pose_graph.nodes)):
135 i_abs = fragment_id * config['n_frames_per_fragment'] + i
136 print(
137 "Fragment %03d / %03d :: integrate rgbd frame %d (%d of %d)." %
138 (fragment_id, n_fragments - 1, i_abs, i + 1, len(pose_graph.nodes)))
139 rgbd = read_rgbd_image(color_files[i_abs], depth_files[i_abs], False,
140 config)
141 pose = pose_graph.nodes[i].pose
142 volume.integrate(rgbd, intrinsic, np.linalg.inv(pose))
143 mesh = volume.extract_triangle_mesh()
144 mesh.compute_vertex_normals()
145 return mesh
146
Once the poses are estimates, /tutorial/pipelines/rgbd_integration.ipynb is used to reconstruct a colored fragment from each RGBD sequence.
Batch processing
181# examples/python/reconstruction_system/make_fragments.py
182
183def run(config):
184
185 print("making fragments from RGBD sequence.")
186 make_clean_folder(join(config["path_dataset"], config["folder_fragment"]))
187
188 [color_files, depth_files] = get_rgbd_file_lists(config["path_dataset"])
189 n_files = len(color_files)
190 n_fragments = int(
191 math.ceil(float(n_files) / config['n_frames_per_fragment']))
192
193 if config["python_multi_threading"] is True:
194 from joblib import Parallel, delayed
195 import multiprocessing
196 import subprocess
197 MAX_THREAD = min(multiprocessing.cpu_count(), n_fragments)
198 Parallel(n_jobs=MAX_THREAD)(delayed(process_single_fragment)(
199 fragment_id, color_files, depth_files, n_files, n_fragments, config)
200 for fragment_id in range(n_fragments))
201 else:
202 for fragment_id in range(n_fragments):
203 process_single_fragment(fragment_id, color_files, depth_files,
204 n_files, n_fragments, config)
The main function calls each individual function explained above.
Results
Fragment 000 / 013 :: RGBD matching between frame : 0 and 1
Fragment 000 / 013 :: RGBD matching between frame : 0 and 5
Fragment 000 / 013 :: RGBD matching between frame : 0 and 10
Fragment 000 / 013 :: RGBD matching between frame : 0 and 15
Fragment 000 / 013 :: RGBD matching between frame : 0 and 20
:
Fragment 000 / 013 :: RGBD matching between frame : 95 and 96
Fragment 000 / 013 :: RGBD matching between frame : 96 and 97
Fragment 000 / 013 :: RGBD matching between frame : 97 and 98
Fragment 000 / 013 :: RGBD matching between frame : 98 and 99
The following is a log from optimize_posegraph_for_fragment
.
[GlobalOptimizationLM] Optimizing PoseGraph having 100 nodes and 195 edges.
Line process weight : 389.309502
[Initial ] residual : 3.223357e+05, lambda : 1.771814e+02
[Iteration 00] residual : 1.721845e+04, valid edges : 157, time : 0.022 sec.
[Iteration 01] residual : 1.350251e+04, valid edges : 168, time : 0.017 sec.
:
[Iteration 32] residual : 9.779118e+03, valid edges : 179, time : 0.013 sec.
Current_residual - new_residual < 1.000000e-06 * current_residual
[GlobalOptimizationLM] total time : 0.519 sec.
[GlobalOptimizationLM] Optimizing PoseGraph having 100 nodes and 179 edges.
Line process weight : 398.292104
[Initial ] residual : 5.120047e+03, lambda : 2.565362e+02
[Iteration 00] residual : 5.064539e+03, valid edges : 179, time : 0.014 sec.
[Iteration 01] residual : 5.037665e+03, valid edges : 178, time : 0.015 sec.
:
[Iteration 11] residual : 5.017307e+03, valid edges : 177, time : 0.013 sec.
Current_residual - new_residual < 1.000000e-06 * current_residual
[GlobalOptimizationLM] total time : 0.197 sec.
CompensateReferencePoseGraphNode : reference : 0
The following is a log from integrate_rgb_frames_for_fragment
.
Fragment 000 / 013 :: integrate rgbd frame 0 (1 of 100).
Fragment 000 / 013 :: integrate rgbd frame 1 (2 of 100).
Fragment 000 / 013 :: integrate rgbd frame 2 (3 of 100).
:
Fragment 000 / 013 :: integrate rgbd frame 97 (98 of 100).
Fragment 000 / 013 :: integrate rgbd frame 98 (99 of 100).
Fragment 000 / 013 :: integrate rgbd frame 99 (100 of 100).
The following images show some of the fragments made by this script.



