CoLC (CVPR 2026)

CoLC: Communication-Efficient Collaborative Perception with LiDAR Completion (Paper)

Installation

1. Basic Installation

This code is based on CoAlign, so I recommend you visit CoAlign Installation Guide for details!

Or you can refer to OpenCOOD data introduction and OpenCOOD installation guide to prepare data and install CoAlign. The installation is totally the same as OpenCOOD.

conda create -n colc python=3.7 
conda activate colc

pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116

pip install spconv-cu116

pip install -r requirements.txt

git clone https://github.com/CatOneTwo/CoLC.git
cd CoLC
python setup.py develop

# Bbx IOU cuda version compile
python opencood/utils/setup.py build_ext --inplace 

2. Dependent packages required by CoLC.

(1) Install pointnet2-ops follows Pointnet2, if failed see this issue

(2) Cupoch library

pip install cupoch

Data Preparation

mkdir a dataset folder under CoAlign. Put your V2X-Sim in this folder.

CoLC/dataset

. 
├── V2X-Sim-2.0
│   ├── sweeps
│   └── v2.0-mini
├── v2xsim2_info
│   ├── v2xsim_infos_test.pkl
│   ├── v2xsim_infos_train.pkl
│   └── v2xsim_infos_val.pkl

Note *.pkl file in v2xsim2_info can be found in Google Drive

Training and Inference

Please follow the steps in train_colc.bash to train each module in order and use test_colc.bash for inference. Replace the model-loading paths in the Bash files with your own pretrained or trained model paths as needed.

Citation

@InProceedings{Han_2026_CVPR,
    author    = {Yushan, Han and  Hui, Zhang and Qiming, Xia and Yi, Jin and Yidong,Li},
    title     = {CoLC: Communication-Efficient Collaborative Perception with LiDAR Completion},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2026},
    pages     = {2983-2992}
}

Acknowlege

This project is impossible without the code of OpenCOOD and CoAlign!