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.

翻译：神经辐射场（NeRFs）在视图合成和深度估计等应用中展现了潜力，但从多视角图像学习时面临固有的不确定性。当前用于量化不确定性的方法要么基于启发式，要么计算成本高昂。我们提出BayesRays，这是一种无需修改训练过程即可评估任意预训练NeRF不确定性的后处理框架。该方法通过空间扰动和贝叶斯拉普拉斯近似构建体素不确定性场。我们从统计角度推导了该算法，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。