Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields

Lily Goli, Cody Reading, Silvia Sellán, Alec Jacobson, Andrea Tagliasacchi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 20061-20070

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/

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[bibtex]
@InProceedings{Goli_2024_CVPR, author = {Goli, Lily and Reading, Cody and Sell\'an, Silvia and Jacobson, Alec and Tagliasacchi, Andrea}, title = {Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {20061-20070} }