# Dense Geometry Supervision for Underwater Depth Estimation

arXiv: https://arxiv.org/abs/2504.18233 · HTML: https://mlsanity.com/paper/2504.18233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021)
- [BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation](https://mlsanity.com/paper/2204.00987/) (2022)
- [NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation](https://mlsanity.com/paper/2203.01502/) (2022)
- [SeaThru-NeRF: Neural Radiance Fields in Scattering Media](https://mlsanity.com/paper/2304.07743/) (2023)
- [VA-DepthNet: A Variational Approach to Single Image Depth Prediction](https://mlsanity.com/paper/2302.06556/) (2023)
- [IEBins: Iterative Elastic Bins for Monocular Depth Estimation](https://mlsanity.com/paper/2309.14137/) (2023)
- [Generalized Binary Search Network for Highly-Efficient Multi-View Stereo](https://mlsanity.com/paper/2112.02338/) (2021)
- [UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots](https://mlsanity.com/paper/2209.12358/) (2022)
- [FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets](https://mlsanity.com/paper/2302.12772/) (2023)
- [Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle](https://mlsanity.com/paper/1903.11750/) (2019)

## Abstract (arXiv)

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.