  Self-supervised monocular depth estimation has emerged as a promising method
because it does not require groundtruth depth maps during training. As an
alternative for the groundtruth depth map, the photometric loss enables to
provide self-supervision on depth prediction by matching the input image
frames. However, the photometric loss causes various problems, resulting in
less accurate depth values compared with supervised approaches. In this paper,
we propose SAFENet that is designed to leverage semantic information to
overcome the limitations of the photometric loss. Our key idea is to exploit
semantic-aware depth features that integrate the semantic and geometric
knowledge. Therefore, we introduce multi-task learning schemes to incorporate
semantic-awareness into the representation of depth features. Experiments on
KITTI dataset demonstrate that our methods compete or even outperform the
state-of-the-art methods. Furthermore, extensive experiments on different
datasets show its better generalization ability and robustness to various
conditions, such as low-light or adverse weather.

Computer Vision and Pattern Recognition