水下高斯溅射：一项受控跨场景研究
Gaussian Splatting Underwater: A Controlled Cross-Regime Study
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中文总结 AI 辅助
本文通过受控实验对比5款公开代码的高斯溅射相关系统，探究其在不同浊度、光照的水下场景的表现，发现性能更多依赖设置而非架构，相关代码已公开。
中文摘要 AI 辅助
水下环境对三维重建颇具挑战性，因为水中悬浮颗粒会散射和漫射光线，浊度存在变化，光吸收依赖于波长，且光照极少均匀。基于高斯溅射（Gaussian splatting）的方法通常是为能获得良好图像质量的场景开发的，且主要在相对浅的水域进行测试。本文研究了高斯溅射在不同浊度、光照缺失和颜色衰减的公开水下数据集以及一项工业调查中的表现。在共享位姿、初始化、预算和评估器的同一协议下，运行了5个带有公开代码的系统，以确定它们的相对优势、劣势和局限性。结果表明，这些方法的性能更多取决于设置而非架构。水体透明度在渲染前起作用，因为运动恢复结构（structure-from-motion）在清澈水中可配准99.5%的帧，而在12 NTU时配准率为0.0%。光照几何结构决定了介质模型是否有用：在随相机移动的人造光下，无介质感知的高斯溅射优于两种介质感知系统。在调查中，基准的光度领先者排名最后，其几何性能被普通3DGS前加修复预处理的方法超越——且这些情况均未在该领域报告的分数中体现。场景构建、每次运行的配置和评估代码已在该httpsURL发布。
英文摘要
The underwater environment is challenging for 3D reconstruction, because particles suspended in the water scatter and diffuse light, turbidity varies, absorption depends on wavelength, and illumination is rarely uniform. Methods based on Gaussian splatting have generally been developed for conditions that allow good image quality, and have primarily been tested on relatively shallow water. This paper examines how well Gaussian splatting performs across publicly available underwater datasets representing different degrees of turbidity, loss of illumination, and colour attenuation, together with an industrial survey. Five systems with public code are run under one protocol, with shared poses, initialisation, budget, and evaluator, to establish their relative advantages, disadvantages, and limitations. What these methods can do turns out to depend more on the setup than on the architecture. Water clarity binds upstream of rendering, since structure-from-motion registers 99.5 \% of frames in clear water and 0.0 \% at 12 NTU. Illumination geometry decides whether a medium model helps at all: under an artificial light that moves with the camera, medium-blind splatting beats both medium-aware systems. On the survey the benchmark's photometric leader comes last, beaten on geometry by a restoration pre-pass in front of vanilla 3DGS---and none of it is visible in the scores the field reports. Scene builds, per-run configurations, and evaluation code are released at https://github.com/olayasturias/uw3dgs
