--- license: mit tags: - lidar - place-recognition - flow-matching - dinov2 - bird-eye-view - autonomous-driving - cvpr2026 language: - en library_name: pytorch pipeline_tag: feature-extraction --- # C-LaV: Conditional Latent Velocity Field Denoising for Weather-Robust LiDAR Place Recognition Official model weights for **C-LaV** (CVPR 2026). - 📄 Project page: - 💻 Code: - 🎓 Authors: Xuewei Cao, Jiayue Yang, Zhiwen Zeng, Yanyong Zhang, Yan Xia (University of Science and Technology of China) ## Quick start ```bash git clone https://github.com/Patience-Joey/clav.git cd clav conda env create -f environment.yml && conda activate clav # Download weights from this repo pip install huggingface_hub python -c " from huggingface_hub import hf_hub_download for d in ('kitti', 'nclt', 'boreas'): for f in ('stage2.pt', 'best.pt'): p = hf_hub_download('xueweicao/clav', f'{d}/{f}') print(p) " # Evaluate bash scripts/eval/evaluate_kitti.sh --checkpoint bash scripts/eval/evaluate_nclt.sh --checkpoint bash scripts/eval/evaluate_boreas.sh --checkpoint ``` ## Datasets Trained / evaluated on: - [KITTI](http://www.cvlibs.net/datasets/kitti/) — synthetic rain/fog/snow on the original clear-weather scans (Hahner et al. fog/snow simulation) - [NCLT](http://robots.engin.umich.edu/nclt/) — cross-session evaluation with synthetic adverse weather - [Boreas](https://www.boreas.utias.utoronto.ca/) — real-world rain and snow, cross-pass GPS-aligned pairs ## License & citation Released under MIT. If C-LaV helps your work, please cite: ```bibtex @inproceedings{cao2026clav, title = {C-LaV: Conditional Latent Velocity Field Denoising for Weather-Robust LiDAR Place Recognition}, author = {Cao, Xuewei and Yang, Jiayue and Zeng, Zhiwen and Zhang, Yanyong and Xia, Yan}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2026} } ```
