CVPR 2024highlight65 citations
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
Lily Goli, Cody Reading, Silvia Sellán, Alec Jacobson, Andrea Tagliasacchi
Abstract
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 pretrained 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/
BibTeX
@inproceedings{cvpr2024_bayesraysuncerta,
title = {Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields},
author = {Lily Goli and Cody Reading and Silvia Sellán and Alec Jacobson and Andrea Tagliasacchi},
booktitle = {CVPR 2024},
year = {2024}
}