  Recent research explosion on Neural Radiance Field (NeRF) shows the
encouraging potential to represent complex scenes with neural networks. One
major drawback of NeRF is its prohibitive inference time: Rendering a single
pixel requires querying the NeRF network hundreds of times. To resolve it,
existing efforts mainly attempt to reduce the number of required sampled
points. However, the problem of iterative sampling still exists. On the other
hand, Neural Light Field (NeLF) presents a more straightforward representation
over NeRF in novel view synthesis -- the rendering of a pixel amounts to one
single forward pass without ray-marching. In this work, we present a deep
residual MLP network (88 layers) to effectively learn the light field. We show
the key to successfully learning such a deep NeLF network is to have sufficient
data, for which we transfer the knowledge from a pre-trained NeRF model via
data distillation. Extensive experiments on both synthetic and real-world
scenes show the merits of our method over other counterpart algorithms. On the
synthetic scenes, we achieve 26-35x FLOPs reduction (per camera ray) and 28-31x
runtime speedup, meanwhile delivering significantly better (1.4-2.8 dB average
PSNR improvement) rendering quality than NeRF without any customized
parallelism requirement.
