This is the official repository for our work "[FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units]"(https://arxiv.org/abs/2401.12862)
FedRSU has been accepted to "IEEE Transactions on Intelligent Transportation Systems (T-ITS)"!
conda create --name fedrsu python=3.7 cmake=3.22.1
# torch
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
# pypcd
git clone /~https://github.com/klintan/pypcd.git
cd pypcd
pip install python-lzf
python setup.py install
# pointnet2
cd lib/pointnet2
python3 setup.py install
cd ../../
# others
opencv-python-headless, open3d, matplotlib, tensorboardx, pyyaml, pyquaternionOur dataset is now available through:
- Google Drive
- Baidu Netdisk (提取码: 56js)
Download and put the unzipped four directories in ./data.
The dataset downloaded here is a zip file composed of four separate directories:
Dair-V2X, LUMPI, IPS300+ and Campus.
Due to acknowledgement reasons, we haven't include IPS300+ in this initial version.
We will update it in the next version soon.
conda activate fedrsuFor non-distibuted setting: (Recommended)
> single gpu
python train.py --config ./config/example.yaml --alg fedavg --gpu 0For distributed / Multi-GPU setting:
> multiple gpu
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train.py --config ./example_ddp.yaml --ddp --alg fedavg --gpu 0
@article{fang2024fedrsu,
title={FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units},
author={Fang, Shaoheng and Ye, Rui and Wang, Wenhao and Liu, Zuhong and Wang, Yuxiao and Wang, Yafei and Chen, Siheng and Wang, Yanfeng},
journal={arXiv preprint arXiv:2401.12862},
year={2024}
}