3 research outputs found
nerf2nerf: Pairwise Registration of Neural Radiance Fields
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.i
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
Neural Radiance Fields (NeRFs) have shown promise in applications like view
synthesis and depth estimation, but learning from multiview images faces
inherent uncertainties. Current methods to quantify them are either heuristic
or computationally demanding. We introduce BayesRays, a post-hoc framework to
evaluate uncertainty in any pre-trained NeRF without modifying the training
process. Our method establishes a volumetric uncertainty field using spatial
perturbations and a Bayesian Laplace approximation. We derive our algorithm
statistically and show its superior performance in key metrics and
applications. Additional results available at: https://bayesrays.github.io