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.

翻译：在不同光照条件下重建并重照物体与场景极具挑战性：现有神经渲染方法往往难以处理材料与光线间的复杂相互作用。融入预计算辐射传输技术虽能实现全局光照，但仍难以应对具有次表面散射效应的材料。我们提出一种新颖框架，通过体渲染学习辐射传输场，并利用多种外观线索端到端优化几何结构。该框架以数据驱动方式扩展了重照明与重建能力，使其能处理更广泛的材料类型。最终模型在现有及新型光照条件下均可生成合理的渲染结果。我们将公开代码及包含次表面散射效应物体的新型光场数据集。