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---
license: cc-by-nc-4.0
task_categories:
- object-detection
- robotics
tags:
- robotics
- autonomous driving
- cooperative perception
- sensor fusion
- LiDAR
- Camera
size_categories:
- 100B<n<1T
---
# CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather
[](https://arxiv.org/abs/2507.02245)
[](https://github.com/NingMingHao/CoInfra)
**CoInfra** is a real-world, large-scale cooperative perception system and dataset designed to support research in multi-agent perception, especially under **adverse weather conditions** (snow, rain, freezing rain). It features 14 synchronized infrastructure nodes, each with a LiDAR and dual cameras, deployed across a shared region for full-scene coverage.
## Download Instructions
**Use the huggingface_hub CLI or library**
```bash
pip install huggingface_hub
```
Create a python script in "CoInfra" folder, and run it:
```python
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="MinghaoNing/CoInfra",
repo_type="dataset",
local_dir=".", # <-- download into current folder
local_dir_use_symlinks=False # <-- ensure actual files are copied
)
print("Downloaded to:", path)
```
This will download the full dataset repo (all files) to a local folder.
## Extraction Instructions
The archives are compressed using `zstd` for fast decompression. To extract the dataset, you need to have `zstd` installed. You can install it via package managers like `apt`, `brew`.
**To extract an archive, use the following command:**
```bash
tar --use-compress-program=zstd -xf [archive_file.tar.zst]
```
**To extract all archives in a directory, you can use:**
```bash
for archive in *.tar.zst; do
echo "Extracting $archive..."
tar --use-compress-program=zstd -xf "$archive"
done
```
## 📖 Citation
If you use CoInfra, please cite:
```bibtex
@article
{ning2025coinfra,
title={CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather},
author={Ning, Minghao and Yang, Yufeng and Shu, Keqi and Huang, Shucheng and Zhong, Jiaming and Salehi, Maryam and Rahmani, Mahdi and Lu, Yukun and Sun, Chen and Saleh, Aladdin and Hashemi, Ehsan and Khajepour, Amir},
journal={arXiv preprint arXiv:2507.02245},
url={https://arxiv.org/abs/2507.02245},
year={2025}
}
``` |