Self-Supervised Underwater Monocular Depth Estimation Informed by Multi-Physics Processes

Depth information is crucial for underwater robotic detection and navigation tasks. However, the underwater imaging environment is complex and variable. The images captured by robots are typically sequences or videos with uniform scene content, and the ground-truth of depth is difficult to obtain. This challenge hinders the generalization of existing self-supervised monocular depth estimation (SMDE) schemes for practical underwater detection applications. To address this issue, we propose an SMDE method for underwater images informed by the physical process of optical degradation. Specifically, we developed a further degradation process for underwater images, which can constrain the image restoration process to solve the attenuation coefficient and depth map, and then combine it with the ego-motion based framework to form a self-supervised learning closed loop. Guided by inherent optical properties, this closed-loop can learn depth cues from the underwater image formation model and the geometric relationships involved in view transformation. Experiments demonstrate that the proposed method is reduced by about 9.1% in RMSE index and improved by about 3.5% in threshold accuracy compared with the SOTA method and can adapt to various underwater robot detection scenarios.