  We present a novel approach for digitizing real-world objects by estimating
their geometry, material properties, and environmental lighting from a set of
posed images with fixed lighting. Our method incorporates into Neural Radiance
Field (NeRF) pipelines the split sum approximation used with image-based
lighting for real-time physical-based rendering. We propose modeling the
scene's lighting with a single scene-specific MLP representing pre-integrated
image-based lighting at arbitrary resolutions. We achieve accurate modeling of
pre-integrated lighting by exploiting a novel regularizer based on efficient
Monte Carlo sampling. Additionally, we propose a new method of supervising
self-occlusion predictions by exploiting a similar regularizer based on Monte
Carlo sampling. Experimental results demonstrate the efficiency and
effectiveness of our approach in estimating scene geometry, material
properties, and lighting. Our method is capable of attaining state-of-the-art
relighting quality after only ${\sim}1$ hour of training in a single NVIDIA
A100 GPU.

Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics