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Neural Radiance Fields (NeRF) has become a popular framework for learningimplicit 3D representations and addressing different tasks such as novel-viewsynthesis or depth-map estimation. However, in downstream applications wheredecisions need to be made based on automatic predictions, it is critical toleverage the confidence associated with the model estimations. Whereasuncertainty quantification is a long-standing problem in Machine Learning, ithas been largely overlooked in the recent NeRF literature. In this context, wepropose Stochastic Neural Radiance Fields (S-NeRF), a generalization ofstandard NeRF that learns a probability distribution over all the possibleradiance fields modeling the scene. This distribution allows to quantify theuncertainty associated with the scene information provided by the model. S-NeRFoptimization is posed as a Bayesian learning problem which is efficientlyaddressed using the Variational Inference framework. Exhaustive experimentsover benchmark datasets demonstrate that S-NeRF is able to provide morereliable predictions and confidence values than generic approaches previouslyproposed for uncertainty estimation in other domains.

Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations