发表机构
State Key Laboratory of CAD&CG, Zhejiang University（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
GS-PI提出优化解耦框架，将PBR材质生成转化为3D点云上的几何条件扩散过程，通过多尺度跨视图条件机制实现多视图一致的外观分解，生成可重照明的PBR高斯资产，优于现有逆渲染方法。
AI 中文摘要
高斯泼溅（Gaussian Splatting, GS）在新视角合成方面表现出色，但其编码了烘焙辐射，将光照与几何紧密纠缠，阻碍了其与基于物理的渲染（PBR）流程的无缝集成。现有的逆渲染方法尝试通过联合优化来解耦材质，但常常因目标冲突而导致严重的模糊性和残留光照伪影。为克服这一问题，我们提出了GS-PI，一种新颖的优化解耦框架，将PBR材质生成视为在3D点云上的几何条件扩散过程。通过直接在3D域中操作，我们的方法固有地保证了多视图一致性，绕过了挑战2D扩散方法的严重像素对应问题。我们引入了一种多尺度跨视图条件机制，整合了三个互补组件：全局语义先验、源锚定光度线索和绝对空间学习的视图方向条件信号。该设计有效压缩了复杂的多视图证据，减轻了跨视图投影错位，并成功防止了高光反射烘焙进固有颜色中。通过从预训练的高斯模型中提取点云，利用条件扩散预测PBR属性，并通过可微光栅化将其蒸馏回去，我们生成了完全可重照明的PBR-GS资产。GS-PI优于近期的逆渲染基线，同时用学习到的扩散过程及随后的短时目标驱动蒸馏取代了逐场景的联合光照/BRDF优化，且无需代理网格。
英文摘要
Gaussian Splatting (GS) excels at novel-view synthesis but encodes baked-in radiance, tightly entangling illumination with geometry and preventing seamless integration into physically based rendering (PBR) pipelines. Existing inverse-rendering methods attempt to disentangle materials via joint optimization, but often suffer from competing objectives that cause severe ambiguities and residual lighting artifacts. To overcome this, we present GS-PI, a novel optimization-decoupled framework that casts PBR material generation as a geometry-conditioned diffusion process on 3D point clouds. By operating directly in the 3D domain, our method inherently guarantees multi-view consistency, sidestepping the severe pixel correspondence issues that challenge 2D diffusion approaches. We introduce a multi-scale cross-view conditioning mechanism that integrates three complementary components: a global semantic prior, source-anchored photometric cues, and an absolute spatial learned view-direction conditioning signal. This design efficiently compresses complex multi-view evidence, mitigating cross-view projection misalignment and successfully preventing specular highlights from baking into intrinsic colors. By extracting a point cloud from a pre-trained Gaussian model, predicting PBR attributes via conditional diffusion, and distilling them back through differentiable rasterisation, we yield a fully relightable PBR-GS asset. GS-PI outperforms recent inverse-rendering baselines while replacing per-scene joint illumination/BRDF optimization with a learned diffusion pass followed by a short target-driven distillation, without requiring proxy meshes.
