  Self-supervised monocular depth estimation aims to infer depth information
without relying on labeled data. However, the lack of labeled information poses
a significant challenge to the model's representation, limiting its ability to
capture the intricate details of the scene accurately. Prior information can
potentially mitigate this issue, enhancing the model's understanding of scene
structure and texture. Nevertheless, solely relying on a single type of prior
information often falls short when dealing with complex scenes, necessitating
improvements in generalization performance. To address these challenges, we
introduce a novel self-supervised monocular depth estimation model that
leverages multiple priors to bolster representation capabilities across
spatial, context, and semantic dimensions. Specifically, we employ a hybrid
transformer and a lightweight pose network to obtain long-range spatial priors
in the spatial dimension. Then, the context prior attention is designed to
improve generalization, particularly in complex structures or untextured areas.
In addition, semantic priors are introduced by leveraging semantic boundary
loss, and semantic prior attention is supplemented, further refining the
semantic features extracted by the decoder. Experiments on three diverse
datasets demonstrate the effectiveness of the proposed model. It integrates
multiple priors to comprehensively enhance the representation ability,
improving the accuracy and reliability of depth estimation. Codes are available
at: \url{https://github.com/MVME-HBUT/MPRLNet}
