Instant Uncertainty Calibration of NeRFs Using a Meta-calibrator
(2312.02350)Abstract
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.
Overview
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Neural Radiance Fields (NeRFs) provide high-quality 3D renderings from 2D images but lacked a way to measure prediction confidence.
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FlipNeRF introduced probabilistic modeling to express uncertainty but failed to offer calibrated, reliable measures.
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This paper presents two novel techniques to produce calibrated uncertainties, even with limited data, by training dual models and employing scene-based predictability.
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The calibration methods yield practical benefits in view enhancement and informed view selection, surpassing existing probabilistic NeRF approaches.
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The study benefits real-world applications like autonomous vehicles and robotics by enabling more reliable decision-making where handling uncertainty is crucial.
Neural Radiance Fields (NeRFs) have excelled in the field of novel view synthesis, enabling the creation of stunningly detailed 3D representations from 2D images. However, there's been a pivotal component missing: a reliable way to measure how confident the model is in its predictions.
Researchers have started to introduce probabilistic modeling into NeRF to address this. One standout approach, FlipNeRF, uses a probabilistic distribution to express uncertainty about color values along rays of light. Although this was a leap forward, the uncertainty measures provided by FlipNeRF weren't quite up to snuff—they weren't calibrated, so they didn't accurately reflect the true likelihood of predictions being correct.
To solve this, a new methodology has been developed for generating calibrated uncertainties from NeRF models, particularly in sparse-view settings where there's not enough data to traditionally fit a calibrator. This paper presents two novel techniques that bypass the need for additional data. The first involves training two models per scene, with patches held out for calibration in one model and not in the other, maintaining high image quality. The second builds on the insight that while calibration curves can vary between scenes, there's an underlying regularity that can be captured and used to predict calibration curves based on scene features—without training an additional NeRF model for each new scene.
The proposed calibration framework demonstrates its prowess not just theoretically but also practically, in improving actual applications like view enhancement and informed view selection. Moreover, it does so while outperforming current probabilistic NeRF methods, showcasing significant improvements in capturing true uncertainty in the sparse-view scenario.
In real-world terms, this research elevates the applicability of NeRF across various domains such as autonomous vehicles and robotics, where quantifying prediction confidence is paramount. It allows for more reliable decision-making and better integration of NeRF into systems where interpreting and managing uncertainty is a critical component of their operation.
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