  Recent neural rendering methods have demonstrated accurate view interpolation
by predicting volumetric density and color with a neural network. Although such
volumetric representations can be supervised on static and dynamic scenes,
existing methods implicitly bake the complete scene light transport into a
single neural network for a given scene, including surface modeling,
bidirectional scattering distribution functions, and indirect lighting effects.
In contrast to traditional rendering pipelines, this prohibits changing surface
reflectance, illumination, or composing other objects in the scene.
  In this work, we explicitly model the light transport between scene surfaces
and we rely on traditional integration schemes and the rendering equation to
reconstruct a scene. The proposed method allows BSDF recovery with unknown
light conditions and classic light transports such as pathtracing. By learning
decomposed transport with surface representations established in conventional
rendering methods, the method naturally facilitates editing shape, reflectance,
lighting and scene composition. The method outperforms NeRV for relighting
under known lighting conditions, and produces realistic reconstructions for
relit and edited scenes. We validate the proposed approach for scene editing,
relighting and reflectance estimation learned from synthetic and captured views
on a subset of NeRV's datasets.
