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滴会造成严重的视觉遮挡，可能导致交通事故。同时，水滴也会降低自动驾驶系统中计算机视觉系统的性能。为解决上述问题，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滴遮挡的视觉信息。针对视频水滴去除训练数据匮乏的问题，我们构建了一个包含雨天复杂驾驶场景中模拟水滴的大规模合成数据集。为提高所提方法的泛化能力，我们采用跨模态训练策略，将合成视频与真实世界图像相结合。大量实验表明，所提方法在复杂的真实驾驶场景中具有良好的泛化性能，并取得了最优的水滴去除效果。