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
this https URL

本文提出了在无监督单目深度估计中引入跨域语义信息以提高几何表示的新思路，包括度量学习方法和特征融合模块，并在 KITTI 数据集上全面评估，在弱质地区和物体边界的监督限制下，本文提出的方法优于现有技术。