mlsanityData to 7 Oct 2026

Evidential Neural Radiance Fields

arXiv 2602.23574 ↗First posted 27 Feb 2026cs.CV cs.AI cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Understanding sources of uncertainty is fundamental to trustworthy three-dimensional scene modeling. While recent advances in neural radiance fields (NeRFs) achieve impressive accuracy in scene reconstruction and novel view synthesis, the lack of uncertainty estimation significantly limits their deployment in safety-critical settings. Existing uncertainty quantification methods for NeRFs fail to separately capture both aleatoric and epistemic uncertainties. Among those that do quantify one or the other, many of them either compromise rendering quality or incur significant computational overhead to obtain uncertainty estimates. To address these issues, we introduce Evidential Neural Radiance Fields, a probabilistic approach that seamlessly integrates with the NeRF rendering process, enabling direct quantification of both aleatoric and epistemic uncertainties from a single forward pass. We compare multiple uncertainty quantification methods on three standardized benchmarks, where our approach demonstrates state-of-the-art scene reconstruction fidelity and uncertainty estimation quality. Code is available at https://github.com/KerryDRX/EvidentialNeRF.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 47 references that are in the map. First posted on arXiv 27 Feb 2026.

Builds on

37 of 47 references.

References · 37

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  4. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  5. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  6. 2015Variational Inference with Normalizing Flowscited by 2,161
  7. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  8. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  9. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  10. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  11. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  12. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
25 more
  1. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  2. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  3. 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 667
  4. 2021RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputscited by 614
  5. 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 596
  6. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 569
  7. 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 510
  8. 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
  9. 2021Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesiscited by 464
  10. 2019Deep Evidential Regressioncited by 354
  11. 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 308
  12. 2020Image Matching across Wide Baselines: From Paper to Practicecited by 224
  13. 2024WildGaussians: 3D Gaussian Splatting in the Wildcited by 150
  14. 2022ActiveNeRF: Learning where to See with Uncertainty Estimationcited by 124
  15. 2022EfficientNeRF: Efficient Neural Radiance Fieldscited by 110
  16. 2023RobustNeRF: Ignoring Distractors with Robust Lossescited by 101
  17. 2023Bayes' Rays: Uncertainty Quantification for Neural Radiance Fieldscited by 90
  18. 2024NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wildcited by 85
  19. 2021Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representationscited by 80
  20. 2022Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantificationcited by 71
  21. 2022Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fieldscited by 58
  22. 2023MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse Inputscited by 40
  23. 2021Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributionscited by 39
  24. 2024Neural Radiance Field in Autonomous Driving: A Surveycited by 19
  25. 2023Estimating 3D Uncertainty Field: Quantifying Uncertainty for Neural Radiance Fieldscited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026VisTa3D: A Dataset and Benchmark for Thin Object Reconstruction from Vision, Tactile, and 3D Point Cloudscited by 1
  2. 2026Are Good Generators Good Decision-Makers? Policy Learning for General Interventions via Retargeted Counterfactual Generationcited by 0
  3. 2026Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstructioncited by 0