# Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion

arXiv: https://arxiv.org/abs/2312.12471 · HTML: https://mlsanity.com/paper/2312.12471/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 30 of its 66 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (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)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [DIODE: A Dense Indoor and Outdoor DEpth Dataset](https://mlsanity.com/paper/1908.00463/) (2019)
- [On the uncertainty of self-supervised monocular depth estimation](https://mlsanity.com/paper/2005.06209/) (2020)
- [NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation](https://mlsanity.com/paper/2203.01502/) (2022)
- [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018)
- [iDisc: Internal Discretization for Monocular Depth Estimation](https://mlsanity.com/paper/2304.06334/) (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)
- [Instance Segmentation in the Dark](https://mlsanity.com/paper/2304.14298/) (2023)
- [Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks](https://mlsanity.com/paper/1809.00646/) (2018)
- [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)
- [TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems](https://mlsanity.com/paper/2012.05703/) (2020)
- [UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing](https://mlsanity.com/paper/1912.10269/) (2019)
- [Unsupervised Single Image Underwater Depth Estimation](https://mlsanity.com/paper/1905.10595/) (2019)
- [GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time Rain Streak Removal](https://mlsanity.com/paper/2210.04708/) (2022)
- [Self-Supervised Monocular Depth Underwater](https://mlsanity.com/paper/2210.03206/) (2022)

## Built on by (most cited first, 28 of 28)

- [DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models](https://mlsanity.com/paper/2503.12885/) (2025)
- [UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction](https://mlsanity.com/paper/2410.01517/) (2024)
- [Distilling Monocular Foundation Model for Fine-grained Depth Completion](https://mlsanity.com/paper/2503.16970/) (2025)
- [Diffusion Models in 3D Vision: A Survey](https://mlsanity.com/paper/2410.04738/) (2024)
- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (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)
- [A Unified Image-Dense Annotation Generation Model for Underwater Scenes](https://mlsanity.com/paper/2503.21771/) (2025)
- [3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics Based Appearance-Medium Decoupling](https://mlsanity.com/paper/2505.21238/) (2025)
- [UVLM: Benchmarking Video Language Model for Underwater World Understanding](https://mlsanity.com/paper/2507.02373/) (2025)
- [Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation](https://mlsanity.com/paper/2504.02391/) (2025)
- [Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images](https://mlsanity.com/paper/2504.08253/) (2025)
- [PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation](https://mlsanity.com/paper/2504.17067/) (2025)
- [Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World](https://mlsanity.com/paper/2505.08607/) (2025)
- [BVI-Mamba: Video Enhancement Using a Visual State-Space Model for Low-Light and Underwater Environments](https://mlsanity.com/paper/2604.23655/) (2026)
- [UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation](https://mlsanity.com/paper/2407.17838/) (2024)
- [SPADE: Sparsity Adaptive Depth Estimator for Zero-Shot, Real-Time, Monocular Depth Estimation in Underwater Environments](https://mlsanity.com/paper/2510.25463/) (2025)
- [Wat3R: Underwater 3D Geometry Learning without Annotations](https://mlsanity.com/paper/2607.08772/) (2026)
- [A Practical Approach to Underwater Depth and Surface Normals Estimation](https://mlsanity.com/paper/2410.02072/) (2024)
- [Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models](https://mlsanity.com/paper/2507.02148/) (2025)
- [Tree-Mamba: A Tree-Aware Mamba for Underwater Monocular Depth Estimation](https://mlsanity.com/paper/2507.07687/) (2025)
- [StereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimation](https://mlsanity.com/paper/2602.16915/) (2026)
- [WaterGen: Decoupling Scene and Medium in Underwater Image Generation](https://mlsanity.com/paper/2606.31147/) (2026)
- [Robust Image Processing Techniques for Construction Environment Monitoring Using Underwater Robots](https://mlsanity.com/paper/2607.01915/) (2026)
- [DY-LUT: Depth-Aware YCbCr Lookup Tables for Real-Time Underwater Image Enhancement](https://mlsanity.com/paper/2607.22801/) (2026)
- [π-SUB: A Physics-Informed Synthetic Underwater Benchmark Dataset for Underwater Image Enhancement](https://mlsanity.com/paper/2608.10589/) (2026)
- [VLMs Win a Systematic Evaluation of Underwater Image Reconstruction](https://mlsanity.com/paper/2608.11425/) (2026)
- [Monocular Depth Estimation from a Single Image: Progress and Opportunities](https://mlsanity.com/paper/2609.01172/) (2026)

## Abstract (arXiv)

Monocular depth estimation has experienced significant progress on terrestrial images in recent years, largely due to deep learning advancements. However, it remains inadequate for underwater scenes, primarily because of data scarcity. Given the inherent challenges of light attenuation and backscattering in water, acquiring clear underwater images or precise depth information is notably difficult and costly. Consequently, learning-based approaches often rely on synthetic data or turn to unsupervised or self-supervised methods to mitigate this lack of data. Nonetheless, the performance of these methods is often constrained by the domain gap and looser constraints. In this paper, we propose a novel pipeline for generating photorealistic underwater images using accurate terrestrial depth data. This approach facilitates the training of supervised models for underwater depth estimation, effectively reducing the performance disparity between terrestrial and underwater environments. Contrary to prior synthetic datasets that merely apply style transfer to terrestrial images without altering the scene content, our approach uniquely creates vibrant, non-existent underwater scenes by leveraging terrestrial depth data through the innovative Stable Diffusion model. Specifically, we introduce a unique Depth2Underwater ControlNet, trained on specially prepared Underwater, Depth, Text data triplets, for this generation task. Our newly developed dataset enables terrestrial depth estimation models to achieve considerable improvements, both quantitatively and qualitatively, on unseen underwater images, surpassing their terrestrial pre-trained counterparts. Moreover, the enhanced depth accuracy for underwater scenes also aids underwater image restoration techniques that rely on depth maps, further demonstrating our dataset's utility. The dataset will be available at https://github.com/zkawfanx/Atlantis.