We present a novel Relightable Neural Renderer (RNR) for simultaneous view
synthesis and relighting using multi-view image inputs. Existing neural
rendering (NR) does not explicitly model the physical rendering process and
hence has limited capabilities on relighting. RNR instead models image
formation in terms of environment lighting, object intrinsic attributes, and
light transport function (LTF), each corresponding to a learnable component. In
particular, the incorporation of a physically based rendering process not only
enables relighting but also improves the quality of view synthesis.
Comprehensive experiments on synthetic and real data show that RNR provides a
practical and effective solution for conducting free-viewpoint relighting.

本文提出了一种新颖的可重光的神经渲染器（RNR），可以使用多视图图像输入进行同时视图合成和重光。与现有的神经渲染（NR）不同，RNR 明确建模物理渲染过程，从而提高了调光的能力，并提高了视图合成的质量。对合成和真实数据的全面实验表明，RNR 为进行自由视点重光提供了实用有效的解决方案。