  We introduce a novel training strategy for stereo matching and optical flow
estimation that utilizes image-to-image translation between synthetic and real
image domains. Our approach enables the training of models that excel in real
image scenarios while relying solely on ground-truth information from synthetic
images. To facilitate task-agnostic domain adaptation and the training of
task-specific components, we introduce a bidirectional feature warping module
that handles both left-right and forward-backward directions. Experimental
results show competitive performance over previous domain translation-based
methods, which substantiate the efficacy of our proposed framework, effectively
leveraging the benefits of unsupervised domain adaptation, stereo matching, and
optical flow estimation.
