# Self-Supervised Monocular Depth Underwater

arXiv: https://arxiv.org/abs/2210.03206 · HTML: https://mlsanity.com/paper/2210.03206/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image](https://mlsanity.com/paper/1709.07492/) (2017)
- [Deep Convolutional Neural Fields for Depth Estimation from a Single Image](https://mlsanity.com/paper/1411.6387/) (2014)
- [Self-Supervised Monocular Depth Estimation with Internal Feature Fusion](https://mlsanity.com/paper/2110.09482/) (2021)
- [Unsupervised Single Image Underwater Depth Estimation](https://mlsanity.com/paper/1905.10595/) (2019)
- [Sky Optimization: Semantically aware image processing of skies in low-light photography](https://mlsanity.com/paper/2006.10172/) (2020)

## Built on by (most cited first, 6 of 6)

- [Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion](https://mlsanity.com/paper/2312.12471/) (2023)
- [Osmosis: RGBD Diffusion Prior for Underwater Image Restoration](https://mlsanity.com/paper/2403.14837/) (2024)
- [Gaussian Splashing: Direct Volumetric Rendering Underwater](https://mlsanity.com/paper/2411.19588/) (2024)
- [WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2406.13344/) (2024)
- [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)

## Abstract (arXiv)

Depth estimation is critical for any robotic system. In the past years estimation of depth from monocular images have shown great improvement, however, in the underwater environment results are still lagging behind due to appearance changes caused by the medium. So far little effort has been invested on overcoming this. Moreover, underwater, there are more limitations for using high resolution depth sensors, this makes generating ground truth for learning methods another enormous obstacle. So far unsupervised methods that tried to solve this have achieved very limited success as they relied on domain transfer from dataset in air. We suggest training using subsequent frames self-supervised by a reprojection loss, as was demonstrated successfully above water. We suggest several additions to the self-supervised framework to cope with the underwater environment and achieve state-of-the-art results on a challenging forward-looking underwater dataset.