  Neural Radiance Fields (NeRF) is a popular view synthesis technique that
represents a scene as a continuous volumetric function, parameterized by
multilayer perceptrons that provide the volume density and view-dependent
emitted radiance at each location. While NeRF-based techniques excel at
representing fine geometric structures with smoothly varying view-dependent
appearance, they often fail to accurately capture and reproduce the appearance
of glossy surfaces. We address this limitation by introducing Ref-NeRF, which
replaces NeRF's parameterization of view-dependent outgoing radiance with a
representation of reflected radiance and structures this function using a
collection of spatially-varying scene properties. We show that together with a
regularizer on normal vectors, our model significantly improves the realism and
accuracy of specular reflections. Furthermore, we show that our model's
internal representation of outgoing radiance is interpretable and useful for
scene editing.

Computer Vision and Pattern Recognition;Graphics