mlsanityData to 14 Oct 2026

Zero NeRF: Registration with Zero Overlap

arXiv 2211.12544 ↗First posted 22 Nov 2022cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

We present Zero-NeRF, a projective surface registration method that, to the best of our knowledge, offers the first general solution capable of alignment between scene representations with minimal or zero visual correspondence. To do this, we enforce consistency between visible surfaces of partial and complete reconstructions, which allows us to constrain occluded geometry. We use a NeRF as our surface representation and the NeRF rendering pipeline to perform this alignment. To demonstrate the efficacy of our method, we register real-world scenes from opposite sides with infinitesimal overlaps that cannot be accurately registered using prior methods, and we compare these results against widely used registration methods.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

21 of 40 references.

References · 21

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  3. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  4. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  5. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
  6. 2018On the Spectral Bias of Neural Networkscited by 1,341
  7. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  8. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
  9. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
  10. 2018Open3D: A Modern Library for 3D Data Processingcited by 721
  11. 2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 642
  12. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 321
9 more
  1. 2019PointNetLK: Robust & Efficient Point Cloud Registration using PointNetcited by 233
  2. 2021Self-Calibrating Neural Radiance Fieldscited by 204
  3. 2016Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registrationcited by 159
  4. 2021NeRF--: Neural Radiance Fields Without Known Camera Parameterscited by 155
  5. 20183DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registrationcited by 103
  6. 2019DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registrationcited by 77
  7. 2022NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimationcited by 58
  8. 2020DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registrationcited by 7
  9. 2018Semantic Mapping for Orchard Environments by Merging Two-Sides Reconstructions of Tree Rowscited by 3

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2; 2025: 2.

7 papers.

  1. 2023DReg-NeRF: Deep Registration for Neural Radiance Fieldscited by 21
  2. 2023NeRFuser: Large-Scale Scene Representation by NeRF Fusioncited by 5
  3. 2025Scalable and High-Quality Neural Implicit Representation for 3D Reconstructioncited by 5
  4. 2024Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fieldscited by 4
  5. 2023Registering Neural Radiance Fields as 3D Density Imagescited by 2
  6. 2024Object Registration in Neural Fieldscited by 0
  7. 2025Embodied Spatial Intelligence: from Implicit Scene Modeling to Spatial Reasoningcited by 0