发表机构
Hangzhou Dianzi University; North China Electric Power University; Brown University; State Key Laboratory of CAD&CG, Zhejiang University（杭州电子科技大学; 华北电力大学; 布朗大学; 浙江大学CAD&CG国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合静态与闪光照明的高斯溅射逆渲染方法，通过GS锚定漫反射场和延迟着色，在室内场景中显著提升材质分解与重照明质量。
AI 中文摘要
当仅存在静态照明时，从照片中恢复几何、材质和光照具有高度歧义性。主动照明设置减少了歧义，但需要暗室或专用硬件。相反，我们协同利用随意室内拍摄中的静态和闪光照明，闪光灯可开可关，且各自来自独立视角。闪光残差约束了反照率和BRDF，而静态照明捕捉了闪光遗漏的掠射角高光。以2DGS重建框架为基础，我们的关键贡献是一个GS锚定的漫反射场：在光栅化的2DGS深度处查询哈希编码的MLP。由于它仅依赖于世界位置，因此在3D中具有视图一致性，并允许闪光残差驱动材质分解，而不是被跨视图的alpha混合漂移所吸收。同时，我们使用延迟着色渲染静态照明，使其也能监督材质分解。在五个合成和三个真实室内场景中，我们的方法在漫反射颜色、反照率和粗糙度材质参数以及重照明方面优于六个最近的基线，其中PSNR比次优基线提高了4.17 dB。
英文摘要
Recovering geometry, materials, and lighting from photographs is highly ambiguous when only static illumination is available. Active-lighting setups reduce the ambiguity but require dark rooms or specialized hardware. Instead, we synergize both static and flash lighting from casual indoor capture, with the flash on or off, each from independent viewpoints. The flash residual constrains albedo and the BRDF, while static lighting captures grazing-angle specular highlights that flash misses. With a 2DGS reconstruction framing, our key contribution is a GS-anchored diffuse field: a hash-encoded MLP is queried at the rasterized 2DGS depth. As it depends only on world position, it is view consistent in 3D and allows the flash residual to drive material decomposition instead of being absorbed by alpha-blending drift across views. At the same time, we render static lighting with deferred shading such that it can also supervise material decomposition. On five synthetic and three real indoor scenes, our method outperforms six recent baselines on diffuse color, albedo and roughness material parameters, and in relighting where PSNR improves by 4.17 dB over the next-best baseline.
