title: Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
summary: 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.
paper_authors._id: 64f950084928280883984b24
paper_authors.name: Lily Goli
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-08T09:33:38.592Z
paper_authors._id: 64fa1fd05ca946a0106ad584
paper_authors.avatarUrl: /avatars/46017ad4ad98ef9779617d5ef62476b7.svg
paper_authors.fullname: Lily Goli
paper_authors.type: user
paper_authors.user: lily-goli
paper_authors._id: 64f950084928280883984b25
paper_authors.name: Cody Reading
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-07T06:55:29.083Z
paper_authors._id: 64e9203ac8193a0eef000c12
paper_authors.avatarUrl: /avatars/78d21e0b7732c53672feb17154649dfe.svg
paper_authors.fullname: Cody Reading
paper_authors.type: user
paper_authors.user: codyreading
paper_authors._id: 64f950084928280883984b26
paper_authors.name: Silvia Selllán
paper_authors._id: 64f950084928280883984b27
paper_authors.name: Alec Jacobson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-07T13:09:28.929Z
paper_authors._id: 63321730fece0490c3d0dca0
paper_authors.avatarUrl: /avatars/552d91d82969b8f227210655d790ee1e.svg
paper_authors.fullname: Alec
paper_authors.type: user
paper_authors.user: alecjacobson
paper_authors._id: 64f950084928280883984b28
paper_authors.name: Andrea Tagliasacchi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-08T09:33:46.757Z
paper_authors._id: 6310c97fa23f0327bce7f93d
paper_authors.avatarUrl: /avatars/999c12348ce57d71f15337f50a6b2685.svg
paper_authors.fullname: Andrea
paper_authors.type: user
paper_authors.user: taiya
paper_title: Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Neural Radiance Fields NeRFs view synthesis depth estimation multiview images BayesRays volumetric uncertainty field Bayesian Laplace approximation