TIRPL: Tailored-Made Inverse Rendering for Point-Light Scenes

Abstract

Inverse rendering has been extensively explored with the advent of neural implicit fields. However, existing methods struggle to model global illumination and to fully integrate volume rendering with physically based rendering in a single stage. To address this issue, we propose TIRPL, a tailored-made inverse rendering method for point-light scenes. Our method efficiently combines volume rendering and physically based rendering to jointly optimize scene geometry, materials, and global illumination from multi-view images in a single stage. We design an indirect color network and allow the light intensity to be learnable to estimate global illumination. Additionally, a feature vector and a tailored-made light intensity decay rate are devised to combine volume rendering and physically based rendering in a single stage. Extensive experiments are conducted in both real and synthetic datasets, showing the effectiveness of our method.