FastSAM-assisted representation enhancement for self-supervised monocular depth estimation

In order to address the issue of unsatisfactory performance in self-supervised monocular depth estimation methods when applied to thin structured regions and boundary regions, this paper proposes a method for self-supervised monocular depth estimation based on FastSAM-assisted representation enhancement. Firstly, without the need for extra supervision, FastSAM is presented to supply the depth network with rich semantic information. Secondly, a semantic guidance module (SGM) is proposed to explore the correlation between semantic features and depth features, and to enhance the global feature representation. Furthermore, to enhance the performance of boundary depth estimation, a edge guiding module (EGM) is built to direct the network to focus more on local features. Extensive experiments show that the proposed method outperforms the state-of-the-art methods, especially in depth estimation of thin-structured regions and boundary regions.