Neural radiance fields (NeRF) based solutions for novel view synthesis can achieve state of the art results.
Recent work proposes models that take less time to render, need less training data or take up less space.
However, few papers explore the use of NeRFs in classic rendering scenarios such as rasterization, which
could contribute to wider adoption. Our paper proposes a deep MLP network with fast evaluation times, that
generates view-dependent shadow maps. The network distills the knowledge of an existing NeRF model and
achieves the speedup through the use of neural light fields, by only doing one network forward per ray.