  Neural Radiance Field (NeRF) is a popular method in data-driven 3D
reconstruction. Given its simplicity and high quality rendering, many NeRF
applications are being developed. However, NeRF's big limitation is its slow
speed. Many attempts are made to speeding up NeRF training and inference,
including intricate code-level optimization and caching, use of sophisticated
data structures, and amortization through multi-task and meta learning. In this
work, we revisit the basic building blocks of NeRF through the lens of classic
techniques before NeRF. We propose Voxel-Accelearated NeRF (VaxNeRF),
integrating NeRF with visual hull, a classic 3D reconstruction technique only
requiring binary foreground-background pixel labels per image. Visual hull,
which can be optimized in about 10 seconds, can provide coarse in-out field
separation to omit substantial amounts of network evaluations in NeRF. We
provide a clean fully-pythonic, JAX-based implementation on the popular JaxNeRF
codebase, consisting of only about 30 lines of code changes and a modular
visual hull subroutine, and achieve about 2-8x faster learning on top of the
highly-performative JaxNeRF baseline with zero degradation in rendering
quality. With sufficient compute, this effectively brings down full NeRF
training from hours to 30 minutes. We hope VaxNeRF -- a careful combination of
a classic technique with a deep method (that arguably replaced it) -- can
empower and accelerate new NeRF extensions and applications, with its
simplicity, portability, and reliable performance gains. Codes are available at
https://github.com/naruya/VaxNeRF .
