  Neural radiance fields (NeRF) methods have demonstrated impressive novel view
synthesis performance. The core approach is to render individual rays by
querying a neural network at points sampled along the ray to obtain the density
and colour of the sampled points, and integrating this information using the
rendering equation. Since dense sampling is computationally prohibitive, a
common solution is to perform coarse-to-fine sampling.
  In this work we address a clear limitation of the vanilla coarse-to-fine
approach -- that it is based on a heuristic and not trained end-to-end for the
task at hand. We introduce a differentiable module that learns to propose
samples and their importance for the fine network, and consider and compare
multiple alternatives for its neural architecture. Training the proposal module
from scratch can be unstable due to lack of supervision, so an effective
pre-training strategy is also put forward. The approach, named `NeRF in detail'
(NeRF-ID), achieves superior view synthesis quality over NeRF and the
state-of-the-art on the synthetic Blender benchmark and on par or better
performance on the real LLFF-NeRF scenes. Furthermore, by leveraging the
predicted sample importance, a 25% saving in computation can be achieved
without significantly sacrificing the rendering quality.
