  Self-supervised depth estimation has made a great success in learning depth
from unlabeled image sequences. While the mappings between image and pixel-wise
depth are well-studied in current methods, the correlation between image, depth
and scene semantics, however, is less considered. This hinders the network to
better understand the real geometry of the scene, since the contextual clues,
contribute not only the latent representations of scene depth, but also the
straight constraints for depth map. In this paper, we leverage the two benefits
by proposing the implicit and explicit semantic guidance for accurate
self-supervised depth estimation. We propose a Semantic-aware Spatial Feature
Alignment (SSFA) scheme to effectively align implicit semantic features with
depth features for scene-aware depth estimation. We also propose a
semantic-guided ranking loss to explicitly constrain the estimated depth maps
to be consistent with real scene contextual properties. Both semantic label
noise and prediction uncertainty is considered to yield reliable depth
supervisions. Extensive experimental results show that our method produces high
quality depth maps which are consistently superior either on complex scenes or
diverse semantic categories, and outperforms the state-of-the-art methods by a
significant margin.
