title: Neural Relighting with Subsurface Scattering by Learning the Radiance
    Transfer Gradient
summary: 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_authors._id: 648c000c5bfc81b14f4b771f
paper_authors.name: Shizhan Zhu
paper_authors._id: 648c000c5bfc81b14f4b7720
paper_authors.name: Shunsuke Saito
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-19T11:50:06.742Z
paper_authors._id: 630688edd37ce67e0e50e548
paper_authors.avatarUrl: /avatars/1681e465d7649f67f94e1b69d236cb1e.svg
paper_authors.fullname: Shunsuke Saito
paper_authors.type: user
paper_authors.user: psyth
paper_authors._id: 648c000c5bfc81b14f4b7721
paper_authors.name: Aljaz Bozic
paper_authors._id: 648c000c5bfc81b14f4b7722
paper_authors.name: Carlos Aliaga
paper_authors._id: 648c000c5bfc81b14f4b7723
paper_authors.name: Trevor Darrell
paper_authors._id: 648c000c5bfc81b14f4b7724
paper_authors.name: Christop Lassner
paper_title: Neural Relighting with Subsurface Scattering by Learning the Radiance
    Transfer Gradient
paper_summary: 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_ai_summary: A novel framework for learning radiance transfer using volume rendering and appearance cues improves rendering and relighting of complex materials, including subsurface scattering.
paper_ai_keywords: radiance transfer volume rendering appearance cues global illumination subsurface scattering RenderNet