Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
Neural Radiance Fields (NeRFs) have shown promise in applications like view
synthesis and depth estimation, but learning from multiview images faces
inherent uncertainties. Current methods to quantify them are either heuristic
or computationally demanding. We introduce BayesRays, a post-hoc framework to
evaluate uncertainty in any pre-trained NeRF without modifying the training
process. Our method establishes a volumetric uncertainty field using spatial
perturbations and a Bayesian Laplace approximation. We derive our algorithm
statistically and show its superior performance in key metrics and
applications. Additional results available at: https://bayesrays.github.io.
64f950084928280883984b24
Lily Goli
claimed_verified
2023-09-08T09:33:38.592Z
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/avatars/46017ad4ad98ef9779617d5ef62476b7.svg
Lily Goli
user
lily-goli
64f950084928280883984b25
Cody Reading
claimed_verified
2023-09-07T06:55:29.083Z
64e9203ac8193a0eef000c12
/avatars/78d21e0b7732c53672feb17154649dfe.svg
Cody Reading
user
codyreading
64f950084928280883984b26
Silvia Selllán
64f950084928280883984b27
Alec Jacobson
admin_assigned
2023-09-07T13:09:28.929Z
63321730fece0490c3d0dca0
/avatars/552d91d82969b8f227210655d790ee1e.svg
Alec
user
alecjacobson
64f950084928280883984b28
Andrea Tagliasacchi
claimed_verified
2023-09-08T09:33:46.757Z
6310c97fa23f0327bce7f93d
/avatars/999c12348ce57d71f15337f50a6b2685.svg
Andrea
user
taiya
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
Neural Radiance Fields (NeRFs) have shown promise in applications like view
synthesis and depth estimation, but learning from multiview images faces
inherent uncertainties. Current methods to quantify them are either heuristic
or computationally demanding. We introduce BayesRays, a post-hoc framework to
evaluate uncertainty in any pre-trained NeRF without modifying the training
process. Our method establishes a volumetric uncertainty field using spatial
perturbations and a Bayesian Laplace approximation. We derive our algorithm
statistically and show its superior performance in key metrics and
applications. Additional results available at: https://bayesrays.github.io.
BayesRays is a post-hoc framework that evaluates uncertainty in pre-trained NeRFs using spatial perturbations and Bayesian Laplace approximation, improving key metrics and applications.
Neural Radiance Fields NeRFs view synthesis depth estimation multiview images BayesRays volumetric uncertainty field Bayesian Laplace approximation