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Reconstructing and relighting objects and scenes under varying lightingconditions is challenging: existing neural rendering methods often cannothandle the complex interactions between materials and light. Incorporatingpre-computed radiance transfer techniques enables global illumination, butstill struggles with materials with subsurface scattering effects. We propose anovel framework for learning the radiance transfer field via volume renderingand utilizing various appearance cues to refine geometry end-to-end. Thisframework extends relighting and reconstruction capabilities to handle a widerrange of materials in a data-driven fashion. The resulting models produceplausible rendering results in existing and novel conditions. We will releaseour code and a novel light stage dataset of objects with subsurface scatteringeffects publicly available.

Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient