Neural Radiance Fields have achieved remarkable results for novel view
synthesis but still lack a crucial component: precise measurement of
uncertainty in their predictions. Probabilistic NeRF methods have tried to
address this, but their output probabilities are not typically accurately
calibrated, and therefore do not capture the true confidence levels of the
model. Calibration is a particularly challenging problem in the sparse-view
setting, where additional held-out data is unavailable for fitting a calibrator
that generalizes to the test distribution. In this paper, we introduce the
first method for obtaining calibrated uncertainties from NeRF models. Our
method is based on a robust and efficient metric to calculate per-pixel
uncertainties from the predictive posterior distribution. We propose two
techniques that eliminate the need for held-out data. The first, based on patch
sampling, involves training two NeRF models for each scene. The second is a
novel meta-calibrator that only requires the training of one NeRF model. Our
proposed approach for obtaining calibrated uncertainties achieves
state-of-the-art uncertainty in the sparse-view setting while maintaining image
quality. We further demonstrate our method's effectiveness in applications such
as view enhancement and next-best view selection.

通过引入一种基于鲁棒而高效的指标的方法，从预测后验分布中计算每个像素的不确定性；我们提出了两种消除需保留数据需求的技术，并提出了一种新颖的元校准器，只需要训练一个 NeRF 模型。从而在稀疏视图设置中获得了最新的不确定性，同时保持图像质量。我们在视图增强和最佳视图选择等应用中展示了我们方法的有效性。