Robust Cooperative Perception By Iterative Object Matching and Pose Adjustment
You can refer to OpenCOOD data introduction and OpenCOOD installation guide to prepare data and install RoCo. The installation is totally the same as CoAlign.
mkdir a dataset folder under RoCo. Put your OPV2V, V2XSet, DAIR-V2X data in this folder. You just need to put in the dataset you want to use. RoCo/dataset. All data configurations are the same as CoAlign. For details, please refer to CoAlign.
├── my_dair_v2x
│ ├── v2x_c
│ ├── v2x_i
│ └── v2x_v
├── OPV2V
│ ├── additional
│ ├── test
│ ├── train
│ └── validate
├── V2XSET
│ ├── test
│ ├── train
│ └── validate
Download them and save them to opencood/logs
-
We are improving our project platform based on CoAlign. You just need to replace the
box_align_v2.py
andintermedia_fusion_dataset.py
files. -
If you want to visualize the pose error, use
evaluate_pose_graph.py
in thetool
folder. -
Important: During the graph matching and optimization process, the parameter
candidate_radius
needs to be adjusted according to different datasets. For specific parameter details, refer to the experiments in RoCo.RoCo/models/sub_modules/box_align_v2.py
Line 449 in bf9747b
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The bounding boxes used in RoCo also come from saved files. You can download and save to
opencood/logs
,
This project is impossible without the code of OpenCOOD, g2opy and d3d.
Thanks to @DerrickXuNu and @yifanlu0227 for the great code framework.
Once again, my sincere thanks to @yifanlu0227 for his patient and meticulous help.