用物理模型提升水下3D渲染,实时还原真实色彩与结构
SeaSplat: Representing Underwater Scenes with 3D Gaussian Splatting and a Physically Grounded Image Formation Model
- 结合物理水下成像模型约束3D高斯点云
- 在真实水下场景中实现高质量视角重建与去色差
- 适合水下视觉、海洋探测与实时渲染研究者
我们提出SeaSplat,一种基于物理基础水下成像模型的3D高斯点云方法,用于实现实时水下场景渲染。水下环境因介质效应导致图像在距离和颜色上产生依赖性失真。通过将3D高斯点云(3DGS)与物理驱动的水下成像模型结合,我们在来自美国维尔京群岛海底车辆采集的SeaThru-NeRF数据集及模拟退化的真实场景上进行验证。结果表明,该方法不仅能显著提升带介质条件下的新视角渲染质量,还能恢复场景真实色彩并消除介质影响。此外,该模型生成了更优的深度图,同时保持3D高斯表示带来的高效计算优势。
原文摘要 · Abstract (English)
We introduce SeaSplat, a method to enable real-time rendering of underwater scenes leveraging recent advances in 3D radiance fields. Underwater scenes are challenging visual environments, as rendering through a medium such as water introduces both range and color dependent effects on image capture. We constrain 3D Gaussian Splatting (3DGS), a recent advance in radiance fields enabling rapid training and real-time rendering of full 3D scenes, with a physically grounded underwater image formation model. Applying SeaSplat to the real-world scenes from SeaThru-NeRF dataset, a scene collected by an underwater vehicle in the US Virgin Islands, and simulation-degraded real-world scenes, not only do we see increased quantitative performance on rendering novel viewpoints from the scene with the medium present, but are also able to recover the underlying true color of the scene and restore renders to be without the presence of the intervening medium. We show that the underwater image formation helps learn scene structure, with better depth maps, as well as show that our improvements maintain the significant computational improvements afforded by leveraging a 3D Gaussian representation.
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