Neural Radiance Fields (NeRF) has become a popular framework for learning
implicit 3D representations and addressing different tasks such as novel-view
synthesis or depth-map estimation. However, in downstream applications where
decisions need to be made based on automatic predictions, it is critical to
leverage the confidence associated with the model estimations. Whereas
uncertainty quantification is a long-standing problem in Machine Learning, it
has been largely overlooked in the recent NeRF literature. In this context, we
propose Stochastic Neural Radiance Fields (S-NeRF), a generalization of
standard NeRF that learns a probability distribution over all the possible
radiance fields modeling the scene. This distribution allows to quantify the
uncertainty associated with the scene information provided by the model. S-NeRF
optimization is posed as a Bayesian learning problem which is efficiently
addressed using the Variational Inference framework. Exhaustive experiments
over benchmark datasets demonstrate that S-NeRF is able to provide more
reliable predictions and confidence values than generic approaches previously
proposed for uncertainty estimation in other domains.

提出了 Stochastic Neural Radiance Fields (S-NeRF) 作为一种泛化标准 NeRF 的方法，在机器学习中使用贝叶斯学习框架进行优化，得出可以量化与模型估计相关的不确定性的概率分布，为场景信息提供了更可靠的预测和置信度值。