# UW-3DGS: Underwater 3D Reconstruction with Physics-Aware Gaussian Splatting

arXiv: https://arxiv.org/abs/2508.06169 · HTML: https://mlsanity.com/paper/2508.06169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2025.

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

- [AquaFlow: A Monocular Gaussian Splatting SLAM for Underwater Streaming Reconstruction](https://mlsanity.com/paper/2608.22906/) (2026)
- [Why 3D Reconstruction Breaks Down on Under-Ice AUV Video, and How to Predict It](https://mlsanity.com/paper/2610.19993/) (2026)

## Abstract (arXiv)

Underwater 3D scene reconstruction faces severe challenges from light absorption, scattering, and turbidity, which degrade geometry and color fidelity in traditional methods like Neural Radiance Fields (NeRF). While NeRF extensions such as SeaThru-NeRF incorporate physics-based models, their MLP reliance limits efficiency and spatial resolution in hazy environments. We introduce UW-3DGS, a novel framework adapting 3D Gaussian Splatting (3DGS) for robust underwater reconstruction. Key innovations include: (1) a plug-and-play learnable underwater image formation module using voxel-based regression for spatially varying attenuation and backscatter; and (2) a Physics-Aware Uncertainty Pruning (PAUP) branch that adaptively removes noisy floating Gaussians via uncertainty scoring, ensuring artifact-free geometry. The pipeline operates in training and rendering stages. During training, noisy Gaussians are optimized end-to-end with underwater parameters, guided by PAUP pruning and scattering modeling. In rendering, refined Gaussians produce clean Unattenuated Radiance Images (URIs) free from media effects, while learned physics enable realistic Underwater Images (UWIs) with accurate light transport. Experiments on SeaThru-NeRF and UWBundle datasets show superior performance, achieving PSNR of 27.604, SSIM of 0.868, and LPIPS of 0.104 on SeaThru-NeRF, with ~65% reduction in floating artifacts.