LiDAR sensors are crucial for providing high-resolution 3D point cloud data in autonomous driving systems, enabling precise environmental perception. However, real-world adverse weather conditions, such as rain, fog, and snow, introduce significant noise and interference, degrading the reliability of LiDAR data and the performance of downstream tasks like semantic segmentation. Existing datasets often suffer from limited weather diversity and small dataset sizes, which restrict their effectiveness in training models. Additionally, current deep learning denoising methods, while effective in certain scenarios, often lack interpretability, complicating the ability to understand and validate their decision-making processes. To overcome these limitations, we introduce two large-scale datasets, Weather-KITTI and Weather-NuScenes, which cover three common adverse weather conditions: rain, fog, and snow. These datasets retain the original LiDAR acquisition information and provide point-level semantic labels for rain, fog, and snow. Furthermore, we propose a novel point cloud denoising model, TripleMixer, comprising three mixer layers: the Geometry Mixer Layer, the Frequency Mixer Layer, and the Channel Mixer Layer. These layers are designed to capture geometric spatial information, extract multi-scale frequency information, and enhance the multi-channel feature information of point clouds, respectively. Experiments conducted on the WADS dataset in real-world scenarios, as well as on our proposed Weather-KITTI and Weather-NuScenes datasets, demonstrate that our model achieves state-of-the-art denoising performance. Additionally, our experiments show that integrating the denoising model into existing segmentation frameworks enhances the performance of downstream tasks.The datasets and code will be made publicly available at https://github.com/Grandzxw/TripleMixer.

翻译：LiDAR传感器在自动驾驶系统中对于提供高分辨率三维点云数据至关重要，能够实现精确的环境感知。然而，现实世界中的恶劣天气条件（如雨、雾、雪）会引入显著的噪声和干扰，降低LiDAR数据的可靠性以及下游任务（如语义分割）的性能。现有数据集通常存在天气多样性有限和数据集规模较小的问题，这限制了其在训练模型方面的有效性。此外，当前的深度学习去噪方法虽然在特定场景下有效，但往往缺乏可解释性，使得理解和验证其决策过程变得复杂。为克服这些局限性，我们引入了两个大规模数据集Weather-KITTI和Weather-NuScenes，涵盖雨、雾、雪三种常见恶劣天气条件。这些数据集保留了原始LiDAR采集信息，并为雨、雾、雪提供了点级语义标签。进一步地，我们提出了一种新颖的点云去噪模型TripleMixer，该模型包含三个混合器层：几何混合器层、频率混合器层和通道混合器层。这些层分别设计用于捕获几何空间信息、提取多尺度频率信息以及增强点云的多通道特征信息。在真实场景的WADS数据集以及我们提出的Weather-KITTI和Weather-NuScenes数据集上进行的实验表明，我们的模型实现了最先进的去噪性能。此外，我们的实验显示，将去噪模型集成到现有分割框架中可以提升下游任务的性能。数据集和代码将在https://github.com/Grandzxw/TripleMixer公开提供。