  Existing vision systems for autonomous driving or robots are sensitive to
waterdrops adhered to windows or camera lenses. Most recent waterdrop removal
approaches take a single image as input and often fail to recover the missing
content behind waterdrops faithfully. Thus, we propose a learning-based model
for waterdrop removal with stereo images. To better detect and remove
waterdrops from stereo images, we propose a novel row-wise dilated attention
module to enlarge attention's receptive field for effective information
propagation between the two stereo images. In addition, we propose an attention
consistency loss between the ground-truth disparity map and attention scores to
enhance the left-right consistency in stereo images. Because of related
datasets' unavailability, we collect a real-world dataset that contains stereo
images with and without waterdrops. Extensive experiments on our dataset
suggest that our model outperforms state-of-the-art methods both quantitatively
and qualitatively. Our source code and the stereo waterdrop dataset are
available at
\href{https://github.com/VivianSZF/Stereo-Waterdrop-Removal}{https://github.com/VivianSZF/Stereo-Waterdrop-Removal}
