RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction


Zhuodong Jiang (University of Bristol), Haoran Wang (University of Bristol), Guoxi Huang (University of Bristol), Brett Seymour (National Park Service), Nantheera Anantrasirichai (University of Bristol)
The 35th British Machine Vision Conference
PDFPosterSupplementary

Abstract

Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aquatic environments. This paper presents an enhanced Gaussian Splatting-based framework that improves both the visual quality and geometric accuracy of deep underwater rendering. We propose decoupled learning for RGB channels, guided by the physics of underwater attenuation, to enable more accurate colour restoration. To address sparse-view limitations and improve view consistency, we introduce a frame interpolation strategy with a novel adaptive weighting scheme. Additionally, we introduce a new loss function aimed at reducing noise while preserving edges, which is essential for deep-sea content. We also release a newly collected dataset, Submerged3D, captured specifically in deep-sea environments. Experimental results demonstrate that our framework consistently outperforms state-of-the-art methods with PSNR gains up to 1.90dB, delivering superior perceptual quality and robustness, and offering promising directions for marine robotics and underwater visual analytics. The code of RUSplatting is available at https://github.com/theflash987/RUSplatting and the dataset Submerged3D can be downloaded at https://zenodo.org/records/15482420.

Citation

@inproceedings{Jiang_2025_BMVC,
author    = {Zhuodong Jiang and Haoran Wang and Guoxi Huang and Brett Seymour and Nantheera Anantrasirichai},
title     = {RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_367/paper.pdf}
}


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