mlsanityData to 14 Oct 2026

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields

arXiv 2506.22433 ↗First posted 27 Jun 2025cs.CVcited by 3

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Abstract (arXiv)

We introduce WarpRF, a training-free general-purpose framework for quantifying the uncertainty of radiance fields. Built upon the assumption that photometric and geometric consistency should hold among images rendered by an accurate model, WarpRF quantifies its underlying uncertainty from an unseen point of view by leveraging backward warping across viewpoints, projecting reliable renderings to the unseen viewpoint and measuring the consistency with images rendered there. WarpRF is simple and inexpensive, does not require any training, and can be applied to any radiance field implementation for free. WarpRF excels at both uncertainty quantification and downstream tasks, e.g., active view selection and active mapping, outperforming any existing method tailored to specific frameworks.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 42 references that are in the map. First posted on arXiv 27 Jun 2025.

Builds on

33 of 42 references.

References · 33

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  3. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  4. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  5. 2023ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenescited by 764
  6. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 735
  7. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  8. 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 616
  9. 2021iMAP: Implicit Mapping and Positioning in Real-Timecited by 476
  10. 2021Dense Depth Priors for Neural Radiance Fields from Sparse Input Viewscited by 302
  11. 2024PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstructioncited by 285
  12. 2024DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshingcited by 138
21 more
  1. 2022ActiveNeRF: Learning where to See with Uncertainty Estimationcited by 127
  2. 2023Bayes' Rays: Uncertainty Quantification for Neural Radiance Fieldscited by 91
  3. 2024NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wildcited by 85
  4. 2021Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representationscited by 82
  5. 2022Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantificationcited by 73
  6. 2022Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fieldscited by 60
  7. 2022NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representationscited by 55
  8. 2022Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fieldscited by 54
  9. 2024Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Renderingcited by 35
  10. 2023Active Neural Mappingcited by 31
  11. 2022ActiveRMAP: Radiance Field for Active Mapping And Planningcited by 30
  12. 2023Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimizationcited by 28
  13. 20244D Gaussian Splatting in the Wild with Uncertainty-Aware Regularizationcited by 28
  14. 2024Neural Visibility Field for Uncertainty-Driven Active Mappingcited by 26
  15. 2021On the confidence of stereo matching in a deep-learning era: a quantitative evaluationcited by 23
  16. 2023MACARONS: Mapping And Coverage Anticipation with RGB Online Self-Supervisioncited by 19
  17. 2022SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integrationcited by 17
  18. 2023Estimating 3D Uncertainty Field: Quantifying Uncertainty for Neural Radiance Fieldscited by 15
  19. 2024Manifold Sampling for Differentiable Uncertainty in Radiance Fieldscited by 12
  20. 2024Self-Evolving Depth-Supervised 3D Gaussian Splatting from Rendered Stereo Pairscited by 11
  21. 2024Sources of Uncertainty in 3D Scene Reconstructioncited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstructioncited by 0
  2. 2026FillGS: Filling Observation Gaps in 4D Gaussian Splatting via Viewpoint-Time Selection and Generative Refinementcited by 0
  3. 2026VCURF: Virtual Camera-based Uncertainty of Radiance Fieldscited by 0