# Infinite Leagues Under the Sea: Photorealistic 3D Underwater Terrain Generation by Latent Fractal Diffusion Models

arXiv: https://arxiv.org/abs/2503.06784 · HTML: https://mlsanity.com/paper/2503.06784/

## Key facts

- Cited by 2 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 28 of its 40 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Placed in

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

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [Autoregressive Image Generation without Vector Quantization](https://mlsanity.com/paper/2406.11838/) (2024)
- [LangSplat: 3D Language Gaussian Splatting](https://mlsanity.com/paper/2312.16084/) (2023)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models](https://mlsanity.com/paper/2310.08529/) (2023)
- [Infinite Photorealistic Worlds using Procedural Generation](https://mlsanity.com/paper/2306.09310/) (2023)
- [DarkGS: Learning Neural Illumination and 3D Gaussians Relighting for Robotic Exploration in the Dark](https://mlsanity.com/paper/2403.10814/) (2024)
- [Beyond NeRF Underwater: Learning Neural Reflectance Fields for True Color Correction of Marine Imagery](https://mlsanity.com/paper/2304.03384/) (2023)
- [UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Framework](https://mlsanity.com/paper/2310.11927/) (2023)
- [RecGS: Removing Water Caustic with Recurrent Gaussian Splatting](https://mlsanity.com/paper/2407.10318/) (2024)
- [SplaTraj: Camera Trajectory Generation with Semantic Gaussian Splatting](https://mlsanity.com/paper/2410.06014/) (2024)
- [TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle](https://mlsanity.com/paper/2408.01569/) (2024)
- [Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and 3D Reconstruction from Noisy Video](https://mlsanity.com/paper/2501.14319/) (2025)
- [PhotoReg: Photometrically Registering 3D Gaussian Splatting Models](https://mlsanity.com/paper/2410.05044/) (2024)
- [Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization](https://mlsanity.com/paper/2109.04087/) (2021)

## Built on by (most cited first, 2 of 2)

- [BenthicFlow: Generating Extensible Underwater Environments via Flow Matching](https://mlsanity.com/paper/2608.23173/) (2026)
- [Towards Scaling Marine Perception with Synthetic Data](https://mlsanity.com/paper/2609.20680/) (2026)

## Abstract (arXiv)

This paper tackles the problem of generating representations of underwater 3D terrain. Off-the-shelf generative models, trained on Internet-scale data but not on specialized underwater images, exhibit downgraded realism, as images of the seafloor are relatively uncommon. To this end, we introduce DreamSea, a generative model to generate hyper-realistic underwater scenes. DreamSea is trained on real-world image databases collected from underwater robot surveys. Images from these surveys contain massive real seafloor observations and covering large areas, but are prone to noise and artifacts from the real world. We extract 3D geometry and semantics from the data with visual foundation models, and train a diffusion model that generates realistic seafloor images in RGBD channels, conditioned on novel fractal distribution-based latent embeddings. We then fuse the generated images into a 3D map, building a 3DGS model supervised by 2D diffusion priors which allows photorealistic novel view rendering. DreamSea is rigorously evaluated, demonstrating the ability to robustly generate large-scale underwater scenes that are consistent, diverse, and photorealistic. Our work drives impact in multiple domains, spanning filming, gaming, and robot simulation.