  Supervised depth estimation has achieved high accuracy due to the advanced
deep network architectures. Since the groundtruth depth labels are hard to
obtain, recent methods try to learn depth estimation networks in an
unsupervised way by exploring unsupervised cues, which are effective but less
reliable than true labels. An emerging way to resolve this dilemma is to
transfer knowledge from synthetic images with ground truth depth via domain
adaptation techniques. However, these approaches overlook specific geometric
structure of the natural images in the target domain (i.e., real data), which
is important for high-performing depth prediction. Motivated by the
observation, we propose a geometry-aware symmetric domain adaptation framework
(GASDA) to explore the labels in the synthetic data and epipolar geometry in
the real data jointly. Moreover, by training two image style translators and
depth estimators symmetrically in an end-to-end network, our model achieves
better image style transfer and generates high-quality depth maps. The
experimental results demonstrate the effectiveness of our proposed method and
comparable performance against the state-of-the-art. Code will be publicly
available at: https://github.com/sshan-zhao/GASDA.
