用物理模型压缩水下3D场景，实现高效高质渲染
TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian
- 基于张量化高斯点云，融合水下光传播物理模型
- 仅用少量参数即达顶尖重建质量，渲染更快更省内存
- 适合水下机器人感知与多媒体应用开发者
水下3D场景重建对水下机器人感知与导航等恶劣环境多媒体应用至关重要。现有方法难以准确模拟光在水中传播、介质与物体表面相互作用的复杂关系，且训练与渲染成本高昂。为此，我们提出张量化水下高斯点阵（TUGS），一种基于物理建模的紧凑水下3D表示。TUGS包含物理驱动的自适应介质估计（AME）模块，可精确模拟水下光衰减与后向散射效应，并引入张量化密度优化策略（TDS），在优化过程中高效细化表示。在真实水下数据集上的大量实验表明，TUGS仅用有限参数即可实现卓越重建效果，且渲染速度更快、内存占用更低。代码已开源。
原文摘要 · Abstract (English)
Underwater 3D scene reconstruction is crucial for multimedia applications in adverse environments, such as underwater robotic perception and navigation. However, the complexity of interactions between light propagation, water medium, and object surfaces poses significant difficulties for existing methods in accurately simulating their interplay. Additionally, expensive training and rendering costs limit their practical application. Therefore, we propose Tensorized Underwater Gaussian Splatting (TUGS), a compact underwater 3D representation based on physical modeling of complex underwater light fields. TUGS includes a physics-based underwater Adaptive Medium Estimation (AME) module, enabling accurate simulation of both light attenuation and backscatter effects in underwater environments, and introduces Tensorized Densification Strategies (TDS) to efficiently refine the tensorized representation during optimization. TUGS is able to render high-quality underwater images with faster rendering speeds and less memory usage. Extensive experiments on real-world underwater datasets have demonstrated that TUGS can efficiently achieve superior reconstruction quality using a limited number of parameters. The code is available at https://liamlian0727.github.io/TUGS
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