Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient

• URL: http://arxiv.org/abs/2306.09322v1
• Date: Thu, 15 Jun 2023 17:56:04 GMT
• Title: Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient
• Authors: Shizhan Zhu, Shunsuke Saito, Aljaz Bozic, Carlos Aliaga, Trevor Darrell, Christop Lassner
• Abstract summary: We propose a novel framework for learning the radiance transfer field via volume rendering. We will release our code and a novel light stage dataset of objects with subsurface scattering effects publicly available.
• Score: 73.52585139592398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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