针对水下场景重建难题，提出鲁棒3D高斯点云渲染新方法。
RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction
- 分通道学习结合水下光衰减物理模型，提升色彩还原精度。
- 引入自适应加权插帧策略，显著改善稀疏视角下的视图一致性。
- 在深海数据集Submerged3D上实现最高1.90dB的PSNR提升。
由于水下环境固有的光吸收、散射和视野受限，高质量水下场景重建仍具挑战。本文提出一种增强型基于高斯点云的框架，显著提升深海渲染的视觉质量与几何精度。通过解耦RGB通道的训练并依据水下光衰减物理规律，实现更精准的色彩恢复；为应对稀疏视角问题，设计了一种新型自适应加权插帧策略，提升多视角一致性；同时引入新损失函数，在抑制噪声的同时有效保持边缘细节，对深海内容尤为关键。我们还发布了专门采集于深海环境的新数据集Submerged3D。实验表明，该框架在多个指标上持续优于现有先进方法，最大PSNR提升达1.90dB，展现出优异的感知质量和鲁棒性，为海洋机器人与水下视觉分析提供新方向。代码与数据已开源。
原文摘要 · Abstract (English)
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.
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