对比五种高斯点云方法在不同水下环境下的表现，发现水质比模型架构影响更大。
Gaussian Splatting Underwater: A Controlled Cross-Regime Study
- 统一协议下测试五种公开方法，控制姿态、初始化和预算
- 清水中可注册99.5%帧，12 NTU浊度下仅0.0%可用
- 人工光源下无介质模型反而优于有介质模型，适合工业应用
水下环境对3D重建极具挑战：水中悬浮颗粒散射与漫射光线，浑浊度变化大，吸收随波长而异，光照通常不均匀。基于高斯点云的方法多针对图像质量良好的条件设计，且主要在浅水区域测试。本文在涵盖不同浑浊度、光照损失与颜色衰减的公开水下数据集上，系统评估五种具有公开代码的系统，采用统一协议（共享位姿、初始化、预算与评估器），揭示其相对优劣与局限。结果显示，方法性能更依赖于设置而非架构本身。水质决定渲染前阶段的可行性——清水可注册99.5%帧，12 NTU时降至0.0%。光照几何决定介质模型是否有效：当人工光源随相机移动时，忽略介质的点云方法优于两个考虑介质的系统。工业调研显示，基准摄影测量领先者在几何精度上被前置恢复预处理的普通3DGS超越，且这些差异未反映在领域报告的评分中。场景构建、运行配置与评估代码已开源：https://github.com/olayasturias/uw3dgs。
原文摘要 · Abstract (English)
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
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