Aquatic-GS：一种面向水下场景的混合3D表示方法
Aquatic-GS: A Hybrid 3D Representation for Underwater Scenes
AI总结：
针对水下成像中物体与水体信息耦合导致3D表示困难的问题，提出结合神经水场与扩展3DGS的Aquatic-GS混合表示方法，辅以深度引导优化，在渲染质量、实时速度和图像复原上均优于现有方法。
摘要：
水下3D场景表示是一项有价值但复杂的任务，因为水下成像过程中的衰减和散射效应会使物体与水体的信息高度耦合。这种耦合给现有方法同时有效表示物体和水体介质带来了重大挑战。为解决这一问题，我们提出了Aquatic-GS，这是一种面向水下场景的混合3D表示方法，可同时有效表示物体与水体介质。具体而言，我们构建了神经水场（Neural Water Field，NWF）来隐式建模水体参数，同时扩展最新的3D高斯泼溅（3D Gaussian Splatting，3DGS）来显式建模物体。两个组件通过基于物理的水下成像模型整合，以表示复杂的水下场景。此外，为构建更精确的场景几何结构与细节，我们设计了深度引导优化（Depth-Guided Optimization，DGO）机制，使用伪深度图作为辅助引导。优化完成后，Aquatic-GS可实现水下新视角渲染，并支持还原水下场景的真实外观，仿佛水体介质不存在一般。在模拟数据集与真实世界数据集上开展的大量实验表明，Aquatic-GS性能超越了当前最先进的水下3D表示方法，实现了更优的渲染质量与实时渲染性能，速度提升达410倍。此外，在水下图像复原方面，Aquatic-GS在色彩校正、细节恢复和稳定性上均优于代表性的去水方法。我们的模型、代码和数据集可在https://aquaticgs.github.io获取。
英文摘要：
Representing underwater 3D scenes is a valuable yet complex task, as attenuation and scattering effects during underwater imaging significantly couple the information of the objects and the water. This coupling presents a significant challenge for existing methods in effectively representing both the objects and the water medium simultaneously. To address this challenge, we propose Aquatic-GS, a hybrid 3D representation approach for underwater scenes that effectively represents both the objects and the water medium. Specifically, we construct a Neural Water Field (NWF) to implicitly model the water parameters, while extending the latest 3D Gaussian Splatting (3DGS) to model the objects explicitly. Both components are integrated through a physics-based underwater image formation model to represent complex underwater scenes. Moreover, to construct more precise scene geometry and details, we design a Depth-Guided Optimization (DGO) mechanism that uses a pseudo-depth map as auxiliary guidance. After optimization, Aquatic-GS enables the rendering of novel underwater viewpoints and supports restoring the true appearance of underwater scenes, as if the water medium were absent. Extensive experiments on both simulated and real-world datasets demonstrate that Aquatic-GS surpasses state-of-the-art underwater 3D representation methods, achieving better rendering quality and real-time rendering performance with a 410x increase in speed. Furthermore, regarding underwater image restoration, Aquatic-GS outperforms representative dewatering methods in color correction, detail recovery, and stability. Our models, code, and datasets can be accessed at https://aquaticgs.github.io.
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。
