The waterdrops on windshields during driving can cause severe visual obstructions, which may lead to car accidents. Meanwhile, the waterdrops can also degrade the performance of a computer vision system in autonomous driving. To address these issues, we propose an attention-based framework that fuses the spatio-temporal representations from multiple frames to restore visual information occluded by waterdrops. Due to the lack of training data for video waterdrop removal, we propose a large-scale synthetic dataset with simulated waterdrops in complex driving scenes on rainy days. To improve the generality of our proposed method, we adopt a cross-modality training strategy that combines synthetic videos and real-world images. Extensive experiments show that our proposed method can generalize well and achieve the best waterdrop removal performance in complex real-world driving scenes.

翻译：驾驶过程中挡风玻璃上的水珠会造成严重的视觉遮挡，进而可能引发交通事故。同时，水珠也会降低自动驾驶系统中计算机视觉模块的性能。为解决这些问题，我们提出了一种基于注意力机制的框架，通过融合多帧图像的时空表征来恢复被水珠遮挡的视觉信息。针对视频水珠去除任务缺乏训练数据的问题，我们构建了一个大规模合成数据集，其中包含雨天复杂驾驶场景下模拟生成的水珠。为提升所提方法的泛化能力，我们采用了一种跨模态训练策略，将合成视频与真实世界图像相结合。大量实验表明，所提方法具有良好的泛化性能，在复杂真实驾驶场景中达到了最佳的水珠去除效果。