[TIP 2025] TripleMixer: A Triple-Domain Mixing Model for Point Cloud Denoising under Adverse Weather

Xiongwei Zhao^1*    Congcong Wen^2,3*    Xu Zhu^1#    Yang Wang¹    Haojie Bai¹    Wenhao Dou^1
1Harbin Institute of Technology    ²Harvard University    ³New York University

Abstract

Adverse weather conditions such as snow, fog, and rain pose significant challenges to LiDAR-based perception models by introducing noise and corrupting point cloud measurements. To address this issue, we make the following three contributions:

1. Point cloud denoising network: we propose TripleMixer, a robust and efficient point cloud denoising network that integrates spatial, frequency, and channel-wise processing through three specialized mixer modules. TripleMixer can be seamlessly deployed as a plug-and-play module within existing LiDAR perception pipelines;
2. Large-scale adverse weather datasets: we construct two large-scale simulated datasets, Weather-KITTI and Weather-NuScenes, covering diverse weather scenarios with dense point-wise semantic and noise annotations;
3. LiDAR perception benchmarks: we establish four benchmarks: Denoising, Semantic Segmentation (SS), Place Recognition (PR), and Object Detection (OD). These benchmarks enable systematic evaluation of denoising generalization, transferability, and downstream impact under both simulated and real-world adverse weather conditions.

Updates

• 09/25/2025: Our paper has been accepted by IEEE TIP! 🎉🎉
• 08/22/2025: All codes and configurations have been updated!
• 12/26/2024: The Weather-KITTI and Weather-NuScenes datasets are publicly available on the BaiduPan platform!
  • Weather-KITTI: Download link (code: xxr1)
  • Weather-NuScenes: Download link (code: musq)
• 24/08/2024: Initial release and submitted to the Journal. The dataset will be open source soon!

Outline

• Dataset
• Denoising Network
• LiDAR Perception Benchmarks
• Installation
• Training and Evaluation
• Dataset Generation
• TODO List
• Citation
• License
• Acknowledgements

Dataset

1) Overview

Our Weather-KITTI and Weather-NuScenes are based on the SemanticKITTI and nuScenes-lidarseg datasets, respectively. These datasets cover three common adverse weather conditions: rain, fog, and snow and retain the original LiDAR acquisition information and provide point-level semantic labels for rain, fog, and snow. The visualization results are shown below:

2) Dataset Statistics

Denoising Network

1) Overview

We propose TripleMixer, a plug-and-play point cloud denoising network that integrates spatial, frequency, and channel-wise processing through three specialized mixer layers. TripleMixer enables interpretable and robust denoising under adverse weather conditions, and can be seamlessly integrated into existing LiDAR perception pipelines to enhance their robustness. The overview of the proposed TripleMixer denoising network is shown below:

2) Results Visualization

LiDAR Perception Benchmarks

We establish a Denoising benchmark to evaluate the performance of our denoising model and introduce three downstream LiDAR perception benchmarks: Semantic Segmentation (SS), Place Recognition (PR), and Object Detection (OD), to assess the generalization of state‑of‑the‑art perception models under adverse weather and the effectiveness of our denoising model as a preprocessing step. Notably, in all downstream benchmarks, our denoising model is trained in a supervised manner solely on our Weather‑KITTI and Weather‑NuScenes datasets using only point‑wise weather labels. Meanwhile, all perception models are directly tested on real‑world adverse‑weather datasets without any retraining or fine‑tuning.

1) Denoising

2) Semantic Segmentation (SS)

• Segmentation model selection:

  • SphereFormer, CVPR 2023. ^[Code],
  • SFPNet, ECCV 2024. ^[Code],
  • PointTransformerV3, CVPR 2024. ^[Code],

• Benchmarks Results:

3) Place Recognition (PR)

• Place Recognition model selection:

  • OT, IROS 2022. ^[Code],
  • CVTNet, TII 2023. ^[Code],
  • LPSNet, ICRA 2024. ^[Code],

• Benchmarks Results:

4) Object Detection (OD)

• Detection model selection:

  • TED-S, AAAI 2023. ^[Code],
  • PG-RCNN, ICCV 2023. ^[Code],
  • VoxT-GNN, IPM 2025. ^[Code],

• Benchmarks Results:

Installation

We use the following environment:

conda create -n triplemixer
conda activate triplemixer
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
pip install pyaml==23.12.0 tqdm==4.63.0 scipy==1.8.0 tensorboard==2.16.2
git clone https://github.com/Grandzxw/TripleMixer
cd TripleMixer
pip install -r requirements.txt

Training and Evaluation

1) Training

To train the Wads dataset, run:

python launch_train.py \
--dataset snow_wads \
--path_dataset /path/to/wads/ \
--log_path ./pretrained_models/wads/ \
--config ./configs/Wads.yaml \
--gpu 2 \
--fp16

For other datasets, make the corresponding modifications accordingly.

2) Evaluation and Test

Pre-trained models can be downloaded from Download link

We follow the data preprocessing pipeline of 3D_OutDet (https://github.com/sporsho/3D_OutDet). Before evaluation, please run ./datasets/remove_duplicate.py to remove duplicate point cloud data, and then remap the original labels of the WADS dataset to make them compatible with TripleMixer.

To evaluate the Wads dataset, run:

cd test
python eval_wads.py \
--path_dataset /root/WADS \
--ckpt ./logs/wads/ckpt_best.pth \
--config ./configs/Wads.yaml \
--result_folder ./result/predictions_wads \
--phase test  \
--num_workers 12

To test the Wads dataset IOU, run:

cd test
python test_iou_wads.py 

For other datasets, make the corresponding modifications accordingly.

Dataset Generation

You can generate your own Adverse Weather Dataset on other LiDAR-based point cloud datasets using the code provided in the tools directory of this repository!

TODO List

• Initial release. 🚀
• Add download links for Weather-KITTI and Weather-NuScenes.
• Add Denoising Network code.
• Add train and evaluation script on Adverse Weather Dataset.
• Release checkpoints.
• ...

Citation

If you find our work useful in your research, please consider citing:

@ARTICLE{11262787,
  author={Zhao, Xiongwei and Wen, Congcong and Zhu, Xu and Wang, Yang and Bai, Haojie and Dou, Wenhao},
  journal={IEEE Transactions on Image Processing}, 
  title={TripleMixer: A Triple-Domain Mixing Model for Point Cloud Denoising Under Adverse Weather}, 
  year={2025},
  volume={34},
  number={},
  pages={7712-7727},
  doi={10.1109/TIP.2025.3629047}}

or

@misc{zhao2024triplemixer3dpointcloud,
      title={TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather}, 
      author={Xiongwei Zhao and Congcong Wen and Yang Wang and Haojie Bai and Wenhao Dou},
      year={2024},
      eprint={2408.13802},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.13802}, 
}

or

@article{zhao2024triplemixer,
  title={Triplemixer: A 3d point cloud denoising model for adverse weather},
  author={Zhao, Xiongwei and Wen, Congcong and Wang, Yang and Bai, Haojie and Dou, Wenhao},
  journal={arXiv preprint arXiv:2408.13802},
  year={2024}
}

License

The dataset is based on the SemanticKITTI dataset, provided under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License (CC BY-NC-SA 3.0 US), and the nuScenes-lidarseg dataset, provided under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). This dataset is provided under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

Acknowledgements

This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing! Robo3D, LiDAR_snow_sim, LiDAR_fog_sim, and 3DOutDet.