GS-PI++: Light-Aware Generative PBR Decomposition of Objects in Gaussian Scenes

Recovering physically based surface attributes from Gaussian scenes is an ill-posed inverse problem because observed appearance entangles intrinsic reflectance, surface orientation, and illumination. Object-level inference provides a convenient unit for reusable learned priors, but isolating an object removes contextual observations that can help interpret its appearance, including information about the source illumination and the surrounding scene. Uncertainty in surface orientation further compounds this ambiguity, since errors in normals can be partially compensated by changes in predicted material parameters. We introduce GS-PI++, a light-aware generative framework for PBR decomposition of object instances in Gaussian scenes. For each target object, we construct an object-centered scene appearance probe by removing the target instance and rendering outward from its center. The probe composites visible near-field scene content with the source far-field environment, providing a compact directional observation of the conditions under which the object was observed. Conditioned on the object observations and this probe, a point diffusion model jointly predicts base color, roughness, metallicity, and surface normals. The learned attribute predictor avoids per-scene inverse-rendering optimization. Experiments on synthetic assets and real scanned objects show substantial improvements in material recovery over the evaluated baselines, including comparisons in which source illumination is also provided to competing inverse-rendering methods. The recovered attributes further support downstream Gaussian-based relighting and material editing.