  Neural radiance fields (NeRFs) produce state-of-the-art view synthesis
results. However, they are slow to render, requiring hundreds of network
evaluations per pixel to approximate a volume rendering integral. Baking NeRFs
into explicit data structures enables efficient rendering, but results in a
large increase in memory footprint and, in many cases, a quality reduction. In
this paper, we propose a novel neural light field representation that, in
contrast, is compact and directly predicts integrated radiance along rays. Our
method supports rendering with a single network evaluation per pixel for small
baseline light field datasets and can also be applied to larger baselines with
only a few evaluations per pixel. At the core of our approach is a ray-space
embedding network that maps the 4D ray-space manifold into an intermediate,
interpolable latent space. Our method achieves state-of-the-art quality on
dense forward-facing datasets such as the Stanford Light Field dataset. In
addition, for forward-facing scenes with sparser inputs we achieve results that
are competitive with NeRF-based approaches in terms of quality while providing
a better speed/quality/memory trade-off with far fewer network evaluations.
