This is the official repository of the Weather-KITTI and Weather-NuScenes dataset. For technical details, please refer to:
TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather
Xiongwei Zhao*, Congcong Wen*, Yang Wang,
Haojie Bai, Wenhao Dou.
**[Paper] [Blog][Download[code:xxr1]] **
In this Work, we propose our synthetic adverse weather datasets, named Weather-KITTI and Weather-NuScenes, which 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.
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Continuous Updates！
If you find our work useful in your research, please consider citing:
@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},
}- 24/08/2024: Initial release and submitted to the Journal. The dataset will be open source soon!
- 12/26/2024: BaiduPan for Chinese mainland dataset download link has been updated!
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).