GS-PI: An Optimization-Decoupled Appearance Decomposition Approach for Generating PBR Gaussian Assets
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, sidestepp...
| Authors | Jieting Xu, Rengan Xie, Zijian Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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