In this paper,
we target the application scenario of capturing high-fidelity
assets for neural relighting in controlled studio conditions,
but without requiring a dense light stage. Instead,
we leverage a small number of area lights commonly used
in photogrammetry. We propose ReNeRF, a relightable radiance
field model based on the intuitive and powerful approach
of image-based relighting, which implicitly captures
global light transport (for arbitrary objects) without complex,
error-prone simulations.

Publication Link: https://studios.disneyresearch.com/20...