Dense Geometry Supervision for Underwater Depth Estimation
This work addresses the limited research and data scarcity for monocular depth estimation specifically in underwater scenes, offering a cost-effective solution with practical applications.
The paper tackles the problem of monocular depth estimation in underwater environments by constructing an economically efficient dataset using multi-view depth estimation and introducing a texture-depth fusion module based on underwater optical imaging principles. Experimental results on the FLSea dataset show that their approach significantly improves model accuracy and adaptability in underwater settings.
The field of monocular depth estimation is continually evolving with the advent of numerous innovative models and extensions. However, research on monocular depth estimation methods specifically for underwater scenes remains limited, compounded by a scarcity of relevant data and methodological support. This paper proposes a novel approach to address the existing challenges in current monocular depth estimation methods for underwater environments. We construct an economically efficient dataset suitable for underwater scenarios by employing multi-view depth estimation to generate supervisory signals and corresponding enhanced underwater images. we introduces a texture-depth fusion module, designed according to the underwater optical imaging principles, which aims to effectively exploit and integrate depth information from texture cues. Experimental results on the FLSea dataset demonstrate that our approach significantly improves the accuracy and adaptability of models in underwater settings. This work offers a cost-effective solution for monocular underwater depth estimation and holds considerable promise for practical applications.