  Self-supervised monocular depth estimation has been widely studied, owing to
its practical importance and recent promising improvements. However, most works
suffer from limited supervision of photometric consistency, especially in weak
texture regions and at object boundaries. To overcome this weakness, we propose
novel ideas to improve self-supervised monocular depth estimation by leveraging
cross-domain information, especially scene semantics. We focus on incorporating
implicit semantic knowledge into geometric representation enhancement and
suggest two ideas: a metric learning approach that exploits the
semantics-guided local geometry to optimize intermediate depth representations
and a novel feature fusion module that judiciously utilizes cross-modality
between two heterogeneous feature representations. We comprehensively evaluate
our methods on the KITTI dataset and demonstrate that our method outperforms
state-of-the-art methods. The source code is available at
https://github.com/hyBlue/FSRE-Depth.
