Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification

• URL: http://arxiv.org/abs/2203.10192v1
• Date: Fri, 18 Mar 2022 23:26:20 GMT
• Title: Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification
• Authors: Jianxiong Shen and Antonio Agudo and Francesc Moreno-Noguer and Adria Ruiz
• Abstract summary: Conditional-Flow NeRF (CF-NeRF) is a novel probabilistic framework to incorporate uncertainty quantification into NeRF-based approaches. CF-NeRF learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene.
• Score: 44.598503284186336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A critical limitation of current methods based on Neural Radiance Fields (NeRF) is that they are unable to quantify the uncertainty associated with the learned appearance and geometry of the scene. This information is paramount in real applications such as medical diagnosis or autonomous driving where, to reduce potentially catastrophic failures, the confidence on the model outputs must be included into the decision-making process. In this context, we introduce Conditional-Flow NeRF (CF-NeRF), a novel probabilistic framework to incorporate uncertainty quantification into NeRF-based approaches. For this purpose, our method learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene. In contrast to previous approaches enforcing strong constraints over the radiance field distribution, CF-NeRF learns it in a flexible and fully data-driven manner by coupling Latent Variable Modelling and Conditional Normalizing Flows. This strategy allows to obtain reliable uncertainty estimation while preserving model expressivity. Compared to previous state-of-the-art methods proposed for uncertainty quantification in NeRF, our experiments show that the proposed method achieves significantly lower prediction errors and more reliable uncertainty values for synthetic novel view and depth-map estimation.

Related papers

• Model Free Prediction with Uncertainty Assessment [7.524024486998338]
  We propose a novel framework that transforms the deep estimation paradigm into a platform conducive to conditional mean estimation. We develop an end-to-end convergence rate for the conditional diffusion model and establish the normality of the generated samples. Through numerical experiments, we empirically validate the efficacy of our proposed methodology.
  arXiv  Detail & Related papers  (2024-05-21T11:19:50Z)
• Taming Uncertainty in Sparse-view Generalizable NeRF via Indirect Diffusion Guidance [13.006310342461354]
  Generalizable NeRFs (Gen-NeRF) often produce blurring artifacts in unobserved regions with sparse inputs, which are full of uncertainty. We propose an Indirect Diffusion-guided NeRF framework, termed ID-NeRF, to address this uncertainty from a generative perspective.
  arXiv  Detail & Related papers  (2024-02-02T08:39:51Z)
• FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for Independence-Assumption-Free Uncertainty Estimation [28.899779240902703]
  We propose an independence-assumption-free probabilistic neural radiance field based on Flow-GAN. By combining the generative capability of adversarial learning and the powerful expressivity of normalizing flow, our method explicitly models the density-radiance distribution of the scene. Our method demonstrates state-of-the-art performance by predicting lower rendering errors and more reliable uncertainty on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-09-28T12:05:08Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• The Implicit Delta Method [61.36121543728134]
  In this paper, we propose an alternative, the implicit delta method, which works by infinitesimally regularizing the training loss of uncertainty. We show that the change in the evaluation due to regularization is consistent for the variance of the evaluation estimator, even when the infinitesimal change is approximated by a finite difference.
  arXiv  Detail & Related papers  (2022-11-11T19:34:17Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields [7.380217868660371]
  We show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) We demonstrate that NeRF uncertainty can be utilised for next-best view selection and model refinement.
  arXiv  Detail & Related papers  (2022-09-19T02:28:33Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)
• Stochastic Neural Radiance Fields:Quantifying Uncertainty in Implicit 3D Representations [19.6329380710514]
  Uncertainty quantification is a long-standing problem in Machine Learning. We propose Neural Radiance Fields (S-NeRF), a generalization of standard NeRF that learns a probability distribution over all the possible fields modeling the scene. S-NeRF is able to provide more reliable predictions and confidence values than generic approaches previously proposed for uncertainty estimation in other domains.
  arXiv  Detail & Related papers  (2021-09-05T16:56:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.