GLOW: Global Illumination-Aware Inverse Rendering of Indoor Scenes Captured with Dynamic Co-Located Light & Camera

TL;DR

GLOW is a novel inverse rendering framework that effectively disentangles reflectance and lighting in indoor scenes with dynamic lighting and inter-reflections, leveraging a neural implicit surface and radiance cache.

Contribution

It introduces a global illumination-aware approach with a dynamic radiance cache and specialized regularization to handle complex indoor lighting scenarios.

Findings

Outperforms prior methods in material reflectance estimation

Handles dynamic shadows and near-field lighting effectively

Reduces artifacts in flashlight captures

Abstract

Inverse rendering of indoor scenes remains challenging due to the ambiguity between reflectance and lighting, exacerbated by inter-reflections among multiple objects. While natural illumination-based methods struggle to resolve this ambiguity, co-located light-camera setups offer better disentanglement as lighting can be easily calibrated via Structure-from-Motion. However, such setups introduce additional complexities like strong inter-reflections, dynamic shadows, near-field lighting, and moving specular highlights, which existing approaches fail to handle. We present GLOW, a Global Illumination-aware Inverse Rendering framework designed to address these challenges. GLOW integrates a neural implicit surface representation with a neural radiance cache to approximate global illumination, jointly optimizing geometry and reflectance through carefully designed regularization and initialization. We then introduce a dynamic radiance cache that adapts to sharp lighting discontinuities from near-field motion, and a surface-angle-weighted radiometric loss to suppress specular artifacts common in flashlight captures. Experiments show that GLOW substantially outperforms prior methods in material reflectance estimation under both natural and co-located illumination.