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.
@article{arxiv.2309.01842,
title = {StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation},
author = {Zhexiao Xiong and Feng Qiao and Yu Zhang and Nathan Jacobs},
journal= {arXiv preprint arXiv:2309.01842},
year = {2023}
}