Refine registration¶
Input arguments¶
This script runs with python run_system.py [config] --refine. In [config], ["path_dataset"] should have subfolders fragments which stores fragments in .ply files and a pose graph in a .json file.
The main function runs local_refinement and optimize_posegraph_for_scene. The first function performs pairwise registration on the pairs detected by Register fragments. The second function performs multiway registration.
Fine-grained registration¶
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 | # examples/python/ReconstructionSystem/refine_registration.py
def multiscale_icp(source,
target,
voxel_size,
max_iter,
config,
init_transformation=np.identity(4)):
current_transformation = init_transformation
for i, scale in enumerate(range(len(max_iter))): # multi-scale approach
iter = max_iter[scale]
distance_threshold = config["voxel_size"] * 1.4
print("voxel_size {}".format(voxel_size[scale]))
source_down = source.voxel_down_sample(voxel_size[scale])
target_down = target.voxel_down_sample(voxel_size[scale])
if config["icp_method"] == "point_to_point":
result_icp = o3d.pipelines.registration.registration_icp(
source_down, target_down, distance_threshold,
current_transformation,
o3d.pipelines.registration.TransformationEstimationPointToPoint(
),
o3d.pipelines.registration.ICPConvergenceCriteria(
max_iteration=iter))
else:
source_down.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size[scale] *
2.0,
max_nn=30))
target_down.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size[scale] *
2.0,
max_nn=30))
if config["icp_method"] == "point_to_plane":
result_icp = o3d.pipelines.registration.registration_icp(
source_down, target_down, distance_threshold,
current_transformation,
o3d.pipelines.registration.
TransformationEstimationPointToPlane(),
o3d.pipelines.registration.ICPConvergenceCriteria(
max_iteration=iter))
if config["icp_method"] == "color":
result_icp = o3d.pipelines.registration.registration_colored_icp(
source_down, target_down, voxel_size[scale],
current_transformation,
o3d.pipelines.registration.ICPConvergenceCriteria(
relative_fitness=1e-6,
relative_rmse=1e-6,
max_iteration=iter))
current_transformation = result_icp.transformation
if i == len(max_iter) - 1:
information_matrix = o3d.pipelines.registration.get_information_matrix_from_point_clouds(
source_down, target_down, voxel_size[scale] * 1.4,
result_icp.transformation)
|
Two options are given for the fine-grained registration. The color option is recommended since it uses color information to prevent drift. See [Park2017] for details.
Multiway registration¶
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | # examples/python/ReconstructionSystem/refine_registration.py
def update_posegraph_for_scene(s, t, transformation, information, odometry,
pose_graph):
if t == s + 1: # odometry case
odometry = np.dot(transformation, odometry)
odometry_inv = np.linalg.inv(odometry)
pose_graph.nodes.append(
o3d.pipelines.registration.PoseGraphNode(odometry_inv))
pose_graph.edges.append(
o3d.pipelines.registration.PoseGraphEdge(s,
t,
transformation,
information,
uncertain=False))
else: # loop closure case
pose_graph.edges.append(
o3d.pipelines.registration.PoseGraphEdge(s,
t,
transformation,
information,
uncertain=True))
|
This script uses the technique demonstrated in Multiway registration. Function update_posegraph_for_refined_scene builds a pose graph for multiway registration of all fragments. Each graph node represents a fragment and its pose which transforms the geometry to the global space.
Once a pose graph is built, function optimize_posegraph_for_scene is called for multiway registration.
52 53 54 55 56 57 58 59 60 | # examples/python/ReconstructionSystem/optimize_posegraph.py
def optimize_posegraph_for_refined_scene(path_dataset, config):
pose_graph_name = join(path_dataset, config["template_refined_posegraph"])
pose_graph_optimized_name = join(
path_dataset, config["template_refined_posegraph_optimized"])
run_posegraph_optimization(pose_graph_name, pose_graph_optimized_name,
max_correspondence_distance = config["voxel_size"] * 1.4,
|
Main registration loop¶
The function make_posegraph_for_refined_scene below calls all the functions introduced above.
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 | # examples/python/ReconstructionSystem/refine_registration.py
self.success = False
self.transformation = trans
self.infomation = np.identity(6)
def make_posegraph_for_refined_scene(ply_file_names, config):
pose_graph = o3d.io.read_pose_graph(
join(config["path_dataset"],
config["template_global_posegraph_optimized"]))
n_files = len(ply_file_names)
matching_results = {}
for edge in pose_graph.edges:
s = edge.source_node_id
t = edge.target_node_id
matching_results[s * n_files + t] = \
matching_result(s, t, edge.transformation)
if config["python_multi_threading"].lower() == "true":
from joblib import Parallel, delayed
import multiprocessing
import subprocess
MAX_THREAD = min(multiprocessing.cpu_count(),
max(len(pose_graph.edges), 1))
results = Parallel(n_jobs=MAX_THREAD)(
delayed(register_point_cloud_pair)(
ply_file_names, matching_results[r].s, matching_results[r].t,
matching_results[r].transformation, config)
for r in matching_results)
for i, r in enumerate(matching_results):
matching_results[r].transformation = results[i][0]
matching_results[r].information = results[i][1]
else:
for r in matching_results:
(matching_results[r].transformation,
matching_results[r].information) = \
register_point_cloud_pair(ply_file_names,
matching_results[r].s, matching_results[r].t,
matching_results[r].transformation, config)
pose_graph_new = o3d.pipelines.registration.PoseGraph()
odometry = np.identity(4)
pose_graph_new.nodes.append(
o3d.pipelines.registration.PoseGraphNode(odometry))
for r in matching_results:
(odometry, pose_graph_new) = update_posegraph_for_scene(
matching_results[r].s, matching_results[r].t,
|
The main workflow is: pairwise local refinement -> multiway registration.
Results¶
The pose graph optimization outputs the following messages:
[GlobalOptimizationLM] Optimizing PoseGraph having 14 nodes and 35 edges.
Line process weight : 789.730200
[Initial ] residual : 1.208286e+04, lambda : 1.706306e+01
[Iteration 00] residual : 2.410383e+03, valid edges : 22, time : 0.000 sec.
[Iteration 01] residual : 8.127856e+01, valid edges : 22, time : 0.000 sec.
[Iteration 02] residual : 8.031355e+01, valid edges : 22, time : 0.000 sec.
Delta.norm() < 1.000000e-06 * (x.norm() + 1.000000e-06)
[GlobalOptimizationLM] total time : 0.001 sec.
[GlobalOptimizationLM] Optimizing PoseGraph having 14 nodes and 35 edges.
Line process weight : 789.730200
[Initial ] residual : 8.031355e+01, lambda : 1.716826e+01
Delta.norm() < 1.000000e-06 * (x.norm() + 1.000000e-06)
[GlobalOptimizationLM] total time : 0.000 sec.
CompensateReferencePoseGraphNode : reference : 0
There are 14 fragments and 52 valid matching pairs between fragments. After 23 iterations, 11 edges are detected to be false positives. After they are pruned, pose graph optimization runs again to achieve tight alignment.