# Unsupervised Single Image Underwater Depth Estimation

arXiv: https://arxiv.org/abs/1905.10595 · HTML: https://mlsanity.com/paper/1905.10595/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2019.

## Built on by (most cited first, 16 of 16)

- [Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion](https://mlsanity.com/paper/2312.12471/) (2023)
- [UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots](https://mlsanity.com/paper/2209.12358/) (2022)
- [Osmosis: RGBD Diffusion Prior for Underwater Image Restoration](https://mlsanity.com/paper/2403.14837/) (2024)
- [A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation](https://mlsanity.com/paper/2407.04230/) (2024)
- [Gaussian Splashing: Direct Volumetric Rendering Underwater](https://mlsanity.com/paper/2411.19588/) (2024)
- [Self-Supervised Monocular Depth Underwater](https://mlsanity.com/paper/2210.03206/) (2022)
- [Investigation of the Challenges of Underwater-Visual-Monocular-SLAM](https://mlsanity.com/paper/2306.08738/) (2023)
- [A Unified Image-Dense Annotation Generation Model for Underwater Scenes](https://mlsanity.com/paper/2503.21771/) (2025)
- [WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2406.13344/) (2024)
- [A high-precision underwater object detection based on joint self-supervised deblurring and improved spatial transformer network](https://mlsanity.com/paper/2203.04822/) (2022)
- [UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation](https://mlsanity.com/paper/2407.17838/) (2024)
- [DUViN: Diffusion-Based Underwater Visual Navigation via Knowledge-Transferred Depth Features](https://mlsanity.com/paper/2509.02983/) (2025)
- [A Practical Approach to Underwater Depth and Surface Normals Estimation](https://mlsanity.com/paper/2410.02072/) (2024)
- [Tree-Mamba: A Tree-Aware Mamba for Underwater Monocular Depth Estimation](https://mlsanity.com/paper/2507.07687/) (2025)
- [DichroGAN: Towards Restoration of in-air Colours of Seafloor from Satellite Imagery](https://mlsanity.com/paper/2601.00194/) (2026)
- [VLMs Win a Systematic Evaluation of Underwater Image Reconstruction](https://mlsanity.com/paper/2608.11425/) (2026)

## Abstract (arXiv)

Depth estimation from a single underwater image is one of the most challenging problems and is highly ill-posed. Due to the absence of large generalized underwater depth datasets and the difficulty in obtaining ground truth depth-maps, supervised learning techniques such as direct depth regression cannot be used. In this paper, we propose an unsupervised method for depth estimation from a single underwater image taken `in the wild' by using haze as a cue for depth. Our approach is based on indirect depth-map estimation where we learn the mapping functions between unpaired RGB-D terrestrial images and arbitrary underwater images to estimate the required depth-map. We propose a method which is based on the principles of cycle-consistent learning and uses dense-block based auto-encoders as generator networks. We evaluate and compare our method both quantitatively and qualitatively on various underwater images with diverse attenuation and scattering conditions and show that our method produces state-of-the-art results for unsupervised depth estimation from a single underwater image.