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2026 CVPR CVPR 2026

IR-HGP: Physically-Aware Gaussian Inverse Rendering for High-Illumination Scenes via Generative Priors

Applying 3D Gaussian Splatting to inverse rendering, especially for relightable assets under high-illuminance conditions, remains challenging. Strong specular highlights and complex reflections complicate material-light disentanglement, often baking in shadows and losing specular detail. To address this, we introduce IR-HGP, a framework that achieves robust disentanglement using three synergistic modules: First, a Hybrid Visibility Decomposition module ensures physical visibility consistency. Second, a Generative Illumination Field Prior module infers detailed and high-dynamic range environmental lighting. Finally, a Physics-Aware Radiance Correction module stabilizes optimization and mitigates illumination artifacts. Our framework achieves SOTA material recovery and relighting performance, outperforming existing methods under challenging illumination conditions. It reconstructs the view-dependent "shiny" appearance of reflective surfaces in real time, surpassing the limits of prior 3DGS-based inverse rendering methods.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Computer Science and Computer Vision

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