  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.
