贝叶斯射线：神经辐射场的不确定性量化

Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields

September 6, 2023

作者: Lily Goli, Cody Reading, Silvia Selllán, Alec Jacobson, Andrea Tagliasacchi

cs.AI

摘要

神经辐射场（Neural Radiance Fields，NeRFs）在视图合成和深度估计等应用中表现出潜力，但从多视图图像中学习面临固有的不确定性。目前用于量化这些不确定性的方法要么是启发式的，要么计算成本高。我们引入了BayesRays，这是一个事后框架，用于评估任何预先训练的NeRF中的不确定性，而无需修改训练过程。我们的方法利用空间扰动和贝叶斯拉普拉斯逼近建立体积不确定性场。我们从统计学上推导了我们的算法，并展示了其在关键指标和应用中的卓越性能。更多结果请访问：https://bayesrays.github.io。

English

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.