NEURAL GLOBAL ILLUMINATION FOR INVERSE RENDERING

Rapid progress in scene reconstruction from images should be attributed to the emergence of differentiable renderers. Still, accurate material reconstruction remains a challenge, as it requires modeling indirect light effects. Modern inverse path tracers solve this problem, but are computationally expensive. At the same time, inverse renderers based on real-time graphics ignore indirect light for real-time performance. In this paper, we introduce a novel neural global illumination model, which estimates both direct environment light and indirect light as a surface light field. We build NeGIL, a Monte Carlo differentiable rendering framework based on the proposed model. Our framework effectively handles complex lighting effects (such as inter-reflections) without costly path tracing and facilitates the reconstruction of physically-based spatially-varying materials in an end-to-end manner. Through experiments on the challenging synthetic scenes, we demonstrate that NeGIL significantly outperforms existing light modeling approaches in terms of novel-view synthesis and relighting quality.