TUGS: 基于物理的紧凑表示法用于水下场景的张量高斯表示
TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian
AI总结：
本文提出TUGS，一种基于物理模型的紧凑水下3D表示法，通过物理建模复杂水下光场，实现高效高质量的水下图像渲染，实验表明其在有限参数下能取得优越的重建质量。
摘要：
水下3D场景重建对于恶劣环境下的多媒体应用至关重要，如水下机器人感知与导航。然而，光传播、水介质与物体表面之间的相互作用复杂性给现有方法准确模拟其相互作用带来了重大挑战。此外，昂贵的训练和渲染成本限制了其实际应用。因此，我们提出张量化的水下高斯点绘（TUGS），一种基于复杂水下光场物理建模的紧凑水下3D表示法。TUGS包括基于物理的水下自适应介质估计（AME）模块，能够准确模拟水下环境中光衰减和反散射效应，并引入张量化致密化策略（TDS）以在优化过程中高效细化张量表示。TUGS能够以更快的渲染速度和更少的内存使用生成高质量的水下图像。在真实世界水下数据集上的广泛实验表明，TUGS能够使用有限的参数高效实现优越的重建质量。代码可在https://liamlian0727.github.io/TUGS获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
