Arxiv: https://arxiv.org/abs/2206.04028

If you are interested in our work and use the model or code, please consider cite:

  @inproceedings{
  chen2023co,
  title={{CO}3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving},
  author={Runjian Chen and Yao Mu and Runsen Xu and Wenqi Shao and Chenhan Jiang and Hang Xu and Yu Qiao and Zhenguo Li and Ping Luo},
  booktitle={The Eleventh International Conference on Learning Representations },
  year={2023},
  url={https://openreview.net/forum?id=QUaDoIdgo0}
  }

[2023-02-24] Pre-training code released.

[2022-06-17] Pre-trained backbone models and fine-tuned downstream detection models are now available and can be downloaded here

Please refer to getting_started.md for installation of mmdet3d. We use pytorch 1.8, mmdet 2.22.0 and mmcv 1.4.5 for this project.

• You can download DAIR-V2X dataset from HERE
• Structure of the dataset should be as follows:

CO3
├── mmdet3d
├── tools
├── configs
├── data
│   ├── DAIR-V2X
│   │   ├── cooperative-dataset
│   │   │   ├── cooperative
│   │   │   ├── infrastructure-side
│   │   │   ├── vehicle-side
|   |   │   │

• Preprocess the dataset:

python tools/create_data.py DAIR-V2X-C

python -m torch.distributed.launch --nproc_per_node=8 tools/train.py configs/co3_unsupervised_representation_learning/co3.py --no-validate --launcher pytorch

We use two main codebases for downstream evaluations and 4 3090 GPUs are used for fine-tuning. Note that to use the same backbone for evaluation, we change the original backbone in CenterPoint on Once Benchmark and you can use this config to reproduce the results.

• Once Benchmark: https://github.com/PointsCoder/Once_Benchmark
• OpenPCDet: https://github.com/open-mmlab/OpenPCDet