  NeRF synthesizes novel views of a scene with unprecedented quality by fitting
a neural radiance field to RGB images. However, NeRF requires querying a deep
Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering
times, even on modern GPUs. In this paper, we demonstrate that significant
speed-ups are possible by utilizing thousands of tiny MLPs instead of one
single large MLP. In our setting, each individual MLP only needs to represent
parts of the scene, thus smaller and faster-to-evaluate MLPs can be used. By
combining this divide-and-conquer strategy with further optimizations,
rendering is accelerated by two orders of magnitude compared to the original
NeRF model without incurring high storage costs. Further, using teacher-student
distillation for training, we show that this speed-up can be achieved without
sacrificing visual quality.
