  We introduce a technique for pairwise registration of neural fields that
extends classical optimization-based local registration (i.e. ICP) to operate
on Neural Radiance Fields (NeRF) -- neural 3D scene representations trained
from collections of calibrated images. NeRF does not decompose illumination and
color, so to make registration invariant to illumination, we introduce the
concept of a ''surface field'' -- a field distilled from a pre-trained NeRF
model that measures the likelihood of a point being on the surface of an
object. We then cast nerf2nerf registration as a robust optimization that
iteratively seeks a rigid transformation that aligns the surface fields of the
two scenes. We evaluate the effectiveness of our technique by introducing a
dataset of pre-trained NeRF scenes -- our synthetic scenes enable quantitative
evaluations and comparisons to classical registration techniques, while our
real scenes demonstrate the validity of our technique in real-world scenarios.
Additional results available at: https://nerf2nerf.github.io
