Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient

Shizhan Zhu, Shunsuke Saito, Aljaz Bozic, Carlos Aliaga, Trevor Darrell, Christop Lassner

Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient: 3 upvotes on Hugging Face Daily Papers, #24 of 26 papers on 2023-06-16. Day-by-day upvote history.

Reconstructing and relighting objects and scenes under varying lighting conditions is challenging: existing neural rendering methods often cannot handle the complex interactions between materials and light. Incorporating pre-computed radiance transfer techniques enables global illumination, but still struggles with materials with subsurface scattering effects. We propose a novel framework for learning the radiance transfer field via volume rendering and utilizing various appearance cues to refine geometry end-to-end. This framework extends relighting and reconstruction capabilities to handle a wider range of materials in a data-driven fashion. The resulting models produce plausible rendering results in existing and novel conditions. We will release our code and a novel light stage dataset of objects with subsurface scattering effects publicly available.

Paper page on Hugging Face · arXiv

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