UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction

Abstract

3D Gaussian splatting (3DGS) offers the capability to achieve real-time high quality 3D scene rendering. However 3DGS assumes that the scene is in a clear medium environment and struggles to generate satisfactory representations in underwater scenes where light absorption and scattering are prevalent and moving objects are involved. To overcome these we introduce a novel Gaussian Splatting-based method UW-GS designed specifically for underwater applications. It introduces a color appearance that models distance-dependent color variation employs a new physics-based density control strategy to enhance clarity for distant objects and uses a binary motion mask to handle dynamic content. Optimized with a well-designed loss function supporting for scattering media and strengthened by pseudo-depth maps UW-GS outperforms existing methods with PSNR gains up to 1.26dB. To fully verify the effectiveness of the model we also developed a new underwater dataset S-UW with dynamic object masks. The code of UW-GS and S-UW will be available at https://github.com/WangHaoran16/UW-GS.

Cite

Text

Wang et al. "UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction." Winter Conference on Applications of Computer Vision, 2025.

Markdown

[Wang et al. "UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction." Winter Conference on Applications of Computer Vision, 2025.](https://mlanthology.org/wacv/2025/wang2025wacv-uwgs/)

BibTeX

@inproceedings{wang2025wacv-uwgs,
  title     = {{UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction}},
  author    = {Wang, Haoran and Anantrasirichai, Nantheera and Zhang, Fan and Bull, David},
  booktitle = {Winter Conference on Applications of Computer Vision},
  year      = {2025},
  pages     = {3280-3289},
  url       = {https://mlanthology.org/wacv/2025/wang2025wacv-uwgs/}
}