  Neural Radiance Field training can be accelerated through the use of
grid-based representations in NeRF's learned mapping from spatial coordinates
to colors and volumetric density. However, these grid-based approaches lack an
explicit understanding of scale and therefore often introduce aliasing, usually
in the form of jaggies or missing scene content. Anti-aliasing has previously
been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone
rather than points along a ray, but this approach is not natively compatible
with current grid-based techniques. We show how ideas from rendering and signal
processing can be used to construct a technique that combines mip-NeRF 360 and
grid-based models such as Instant NGP to yield error rates that are 8% - 77%
lower than either prior technique, and that trains 24x faster than mip-NeRF
360.
