GS-ID: Illumination Decomposition on Gaussian Splatting via Adaptive Light Aggregation and Diffusion-Guided Material Priors

Gaussian Splatting (GS) has become an effective representation for photorealistic rendering, but the information about geometry, material, and lighting is entangled and requires illumination decomposition for editing.Current GS-based approaches face significant challenges in disentangling complex light-geometry-material interactions under non-Lambertian conditions, particularly when handling specular reflections and shadows.We present GS-ID, a novel end-to-end framework that achieves comprehensive illumination decomposition by integrating adaptive light aggregation with diffusion-based material priors.In addition to a learnable environment map that captures ambient illumination, we model complex local lighting conditions by adaptively aggregating a set of anisotropic and spatially-varying spherical Gaussian mixtures during optimization.To better model shadow effects, we associate a learnable unit vector with each splat to represent how multiple light sources cause the shadow, further enhancing lighting and material estimation.Together with intrinsic priors from diffusion models, GS-ID significantly reduces light-geometry-material ambiguity and achieves state-of-the-art illumination decomposition performance.Experiments also show that GS-ID effectively supports various downstream applications such as relighting and scene composition.