EvenSplat：曝光与光照变化下高斯溅射的二维-三维耦合分解

中文摘要 AI 辅助

在均匀光照下拍摄的表面，从任何角度观察都呈现几乎相同的外观；而同一表面在非均匀光照下则并非如此。视图之间的曝光会发生变化，单幅图像内的光照也会变化，局部强光源会使一个区域明亮而相邻区域处于阴影中。三维高斯溅射等多视图重建方法将这些光照伪影视为场景的属性，将采集特定的光照与它们恢复的几何和颜色纠缠在一起。我们提出了EvenSplat，一个将这两者分离的框架。EvenSplat将图像空间的光照分解与由高斯携带的光照场耦合起来，使得场景的二维和三维视图共享相同的照明解释；一个相机响应网络和一个局部曝光补偿模块吸收了训练图像中剩余的全局和残差差异。通过在多个数据集和多种非均匀光照形式（跨视图曝光、空间光照变化和高对比度光照）上进行的广泛实验，涵盖真实世界采集和模拟基准，EvenSplat总体上优于最先进的方法，特别是在高对比度光照下。

英文摘要

A surface photographed under even light presents nearly the same appearance from every angle; the same surface under uneven light does not. Exposure changes between views, illumination varies within a single image, and locally strong light sources leave one region bright and its neighbor in shadow. Multi-view reconstruction methods such as 3D Gaussian Splatting treat these lighting artifacts as if they were properties of the scene, entangling capture-specific illumination with the geometry and color they recover. We present EvenSplat, a framework that separates the two. EvenSplat couples an image-space illumination decomposition with an illumination field carried by the Gaussians, so that the same explanation of the lighting is shared between the two-dimensional and three-dimensional views of the scene; a camera-response network and a local exposure-compensation module absorb the global and residual differences that remain across training images. Through extensive experiments across multiple datasets and diverse forms of uneven illumination (cross-view exposure, spatial illumination variation, and high-contrast lighting) on both real-world captured and simulated benchmarks, EvenSplat generally outperforms state-of-the-art methods, particularly under high-contrast illumination.

发表机构

• University of East Anglia（东英吉利大学）
• University of Tokyo（东京大学）

机构由 AI 辅助整理，请以论文原文为准。