mlsanityData to 7 Oct 2026

VF-NeRF: Viewshed Fields for Rigid NeRF Registration

arXiv 2404.03349 ↗First posted 4 Apr 2024cs.CVcited by 1

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Abstract (arXiv)

3D scene registration is a fundamental problem in computer vision that seeks the best 6-DoF alignment between two scenes. This problem was extensively investigated in the case of point clouds and meshes, but there has been relatively limited work regarding Neural Radiance Fields (NeRF). In this paper, we consider the problem of rigid registration between two NeRFs when the position of the original cameras is not given. Our key novelty is the introduction of Viewshed Fields (VF), an implicit function that determines, for each 3D point, how likely it is to be viewed by the original cameras. We demonstrate how VF can help in the various stages of NeRF registration, with an extensive evaluation showing that VF-NeRF achieves SOTA results on various datasets with different capturing approaches such as LLFF and Objaverese.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 57 references that are in the map. First posted on arXiv 4 Apr 2024.

Builds on

31 of 57 references.

References · 31

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  5. 2016Density estimation using Real NVPcited by 2,029
  6. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  7. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  8. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  9. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  10. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  11. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  12. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 732
19 more
  1. 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
  2. 2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 641
  3. 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 596
  4. 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 510
  5. 2021In-Place Scene Labelling and Understanding with Implicit Scene Representationcited by 411
  6. 2020INeRF: Inverting Neural Radiance Fields for Pose Estimationcited by 381
  7. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  8. 2020Deep Global Registrationcited by 228
  9. 2022DynIBaR: Neural Dynamic Image-Based Renderingcited by 224
  10. 2022Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representationscited by 195
  11. 2022Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentationcited by 152
  12. 2021NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenescited by 119
  13. 2021Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representationscited by 80
  14. 2022Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantificationcited by 71
  15. 2022Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fieldscited by 58
  16. 2022Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fieldscited by 58
  17. 2022nerf2nerf: Pairwise Registration of Neural Radiance Fieldscited by 39
  18. 2023DReg-NeRF: Deep Registration for Neural Radiance Fieldscited by 21
  19. 2021DeepBBS: Deep Best Buddies for Point Cloud Registrationcited by 2

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Scene Grounding In the Wildcited by 1