Instant Uncertainty Calibration of NeRFs Using a Meta-calibrator
arxiv(2023)
摘要
Although Neural Radiance Fields (NeRFs) have markedly improved novel view
synthesis, accurate uncertainty quantification in their image predictions
remains an open problem. The prevailing methods for estimating uncertainty,
including the state-of-the-art Density-aware NeRF Ensembles (DANE) [29],
quantify uncertainty without calibration. This frequently leads to over- or
under-confidence in image predictions, which can undermine their real-world
applications. In this paper, we propose a method which, for the first time,
achieves calibrated uncertainties for NeRFs. To accomplish this, we overcome a
significant challenge in adapting existing calibration techniques to NeRFs: a
need to hold out ground truth images from the target scene, reducing the number
of images left to train the NeRF. This issue is particularly problematic in
sparse-view settings, where we can operate with as few as three images. To
address this, we introduce the concept of a meta-calibrator that performs
uncertainty calibration for NeRFs with a single forward pass without the need
for holding out any images from the target scene. Our meta-calibrator is a
neural network that takes as input the NeRF images and uncalibrated uncertainty
maps and outputs a scene-specific calibration curve that corrects the NeRF's
uncalibrated uncertainties. We show that the meta-calibrator can generalize on
unseen scenes and achieves well-calibrated and state-of-the-art uncertainty for
NeRFs, significantly beating DANE and other approaches. This opens
opportunities to improve applications that rely on accurate NeRF uncertainty
estimates such as next-best view planning and potentially more trustworthy
image reconstruction for medical diagnosis.
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