  Recently proposed DNN-based stereo matching methods that learn priors
directly from data are known to suffer a drastic drop in accuracy in new
environments. Although supervised approaches with ground truth disparity maps
often work well, collecting them in each deployment environment is cumbersome
and costly. For this reason, many unsupervised domain adaptation methods based
on image-to-image translation have been proposed, but these methods do not
preserve the geometric structure of a stereo image pair because the
image-to-image translation is applied to each view separately. To address this
problem, in this paper, we propose an attention mechanism that aggregates
features in the left and right views, called Stereoscopic Cross Attention
(SCA). Incorporating SCA to an image-to-image translation network makes it
possible to preserve the geometric structure of a stereo image pair in the
process of the image-to-image translation. We empirically demonstrate the
effectiveness of the proposed unsupervised domain adaptation based on the
image-to-image translation with SCA.
