让野外拍摄的3D场景能随意换光照，还更真实。
R3GW: Relightable 3D Gaussians for Outdoor Scenes in the Wild
- 将场景分前景（可反光）和背景（天空），用两组高斯点分别建模
- 在变化光照下实现物理正确的新视角合成，渲染质量领先
- 适合需要真实光影重演的户外场景重建，如影视、AR应用
3D高斯点阵（3DGS）在静态场景的3D重建与新视角合成中表现优异，但未显式建模光照，难以支持重光照。此外，其在非受控野外照片集（光照多变）下的重建效果不佳。本文提出R3GW，一种针对野外户外场景的可重光照3DGS方法。该方法将场景分离为可反射的前景与非反射的背景（天空），分别使用两组高斯点表示。通过结合物理渲染与3DGS，在动态光照条件下建模前景反射的视点依赖光照效应。在NeRF-OSR数据集上进行定量与定性评估，结果表明本方法达到当前最优性能，支持任意光照条件下的逼真新视角生成。同时，天空的精确建模有效缓解了天空-前景边界的深度重建伪影，显著提升渲染质量。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has established itself as a leading technique for 3D reconstruction and novel view synthesis of static scenes, achieving outstanding rendering quality and fast training. However, the method does not explicitly model the scene illumination, making it unsuitable for relighting tasks. Furthermore, 3DGS struggles to reconstruct scenes captured in the wild by unconstrained photo collections featuring changing lighting conditions. In this paper, we present R3GW, a novel method that learns a relightable 3DGS representation of an outdoor scene captured in the wild. Our approach separates the scene into a relightable foreground and a non-reflective background (the sky), using two distinct sets of Gaussians. R3GW models view-dependent lighting effects in the foreground reflections by combining Physically Based Rendering with the 3DGS scene representation in a varying illumination setting. We evaluate our method quantitatively and qualitatively on the NeRF-OSR dataset, offering state-of-the-art performance and enhanced support for physically-based relighting of unconstrained scenes. Our method synthesizes photorealistic novel views under arbitrary illumination conditions. Additionally, our representation of the sky mitigates depth reconstruction artifacts, improving rendering quality at the sky-foreground boundary
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