  Self-supervised depth estimation has shown its great effectiveness in
producing high quality depth maps given only image sequences as input. However,
its performance usually drops when estimating on border areas or objects with
thin structures due to the limited depth representation ability. In this paper,
we address this problem by proposing a semantic-guided depth representation
enhancement method, which promotes both local and global depth feature
representations by leveraging rich contextual information. In stead of a single
depth network as used in conventional paradigms, we propose an extra semantic
segmentation branch to offer extra contextual features for depth estimation.
Based on this framework, we enhance the local feature representation by
sampling and feeding the point-based features that locate on the semantic edges
to an individual Semantic-guided Edge Enhancement module (SEEM), which is
specifically designed for promoting depth estimation on the challenging
semantic borders. Then, we improve the global feature representation by
proposing a semantic-guided multi-level attention mechanism, which enhances the
semantic and depth features by exploring pixel-wise correlations in the
multi-level depth decoding scheme. Extensive experiments validate the distinct
superiority of our method in capturing highly accurate depth on the challenging
image areas such as semantic category borders and thin objects. Both
quantitative and qualitative experiments on KITTI show that our method
outperforms the state-of-the-art methods.
