mlsanityData to 7 Oct 2026

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

arXiv 2109.00182 ↗First posted 1 Sep 2021cs.CVcited by 68

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Abstract (arXiv)

In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to most existing local descriptors which rely on a fragile local reference frame to gain rotation invariance, the proposed descriptor achieves the rotation invariance by recent technologies of group equivariant feature learning, which brings more robustness to point density and noise. Meanwhile, the descriptor in YOHO also has a rotation equivariant part, which enables us to estimate the registration from just one correspondence hypothesis. Such property reduces the searching space for feasible transformations, thus greatly improves both the accuracy and the efficiency of YOHO. Extensive experiments show that YOHO achieves superior performances with much fewer needed RANSAC iterations on four widely-used datasets, the 3DMatch/3DLoMatch datasets, the ETH dataset and the WHU-TLS dataset. More details are shown in our project page: https://hpwang-whu.github.io/YOHO/.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). Builds on 57 of its 99 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

57 of 99 references.

References · 57

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2016Group Equivariant Convolutional Networkscited by 1,154
  3. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 983
  4. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  5. 2018Open3D: A Modern Library for 3D Data Processingcited by 717
  6. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  7. 2018Spherical CNNscited by 452
  8. 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 333
  9. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  10. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  11. 2016Steerable CNNscited by 298
  12. 2016Harmonic Networks: Deep Translation and Rotation Equivariancecited by 293
38 more
  1. 2020PREDATOR: Registration of 3D Point Clouds with Low Overlapcited by 287
  2. 2020TEASER: Fast and Certifiable Point Cloud Registrationcited by 274
  3. 2019Gauge Equivariant Convolutional Networks and the Icosahedral CNNcited by 262
  4. 2017Learning SO(3) Equivariant Representations with Spherical CNNscited by 244
  5. 2019PointNetLK: Robust & Efficient Point Cloud Registration using PointNetcited by 231
  6. 2020Deep Global Registrationcited by 228
  7. 2017Learning Steerable Filters for Rotation Equivariant CNNscited by 193
  8. 2018PPFNet: Global Context Aware Local Features for Robust 3D Point Matchingcited by 180
  9. 2020D3Feat: Joint Learning of Dense Detection and Description of 3D Local Featurescited by 175
  10. 2018The Perfect Match: 3D Point Cloud Matching with Smoothed Densitiescited by 169
  11. 2016Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registrationcited by 159
  12. 2019PRNet: Self-Supervised Learning for Partial-to-Partial Registrationcited by 146
  13. 2021PointDSC: Robust Point Cloud Registration using Deep Spatial Consistencycited by 142
  14. 2020SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registrationcited by 141
  15. 2018PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptorscited by 140
  16. 2019SDM-NET: Deep Generative Network for Structured Deformable Meshcited by 113
  17. 2020Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondencescited by 108
  18. 2019B-Spline CNNs on Lie Groupscited by 97
  19. 20193DRegNet: A Deep Neural Network for 3D Point Registrationcited by 96
  20. 2021Robust Point Cloud Registration Framework Based on Deep Graph Matchingcited by 96
  21. 2020DeepGMR: Learning Latent Gaussian Mixture Models for Registrationcited by 91
  22. 2017Polar Transformer Networkscited by 81
  23. 2021Equivariant Point Network for 3D Point Cloud Analysiscited by 81
  24. 2019DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registrationcited by 77
  25. 2019Effective Rotation-invariant Point CNN with Spherical Harmonics kernelscited by 57
  26. 2018A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Drivingcited by 54
  27. 2020Spin-Weighted Spherical CNNscited by 50
  28. 2019GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNscited by 47
  29. 2020End-to-End Learning Local Multi-view Descriptors for 3D Point Cloudscited by 47
  30. 2019Equivariant Multi-View Networkscited by 44
  31. 2019Quaternion Equivariant Capsule Networks for 3D Point Cloudscited by 41
  32. 2020Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloudcited by 38
  33. 2018Group Equivariant Capsule Networkscited by 31
  34. 20193D-Rotation-Equivariant Quaternion Neural Networkscited by 30
  35. 2020Canonical Capsules: Self-Supervised Capsules in Canonical Posecited by 30
  36. 2019SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentationcited by 29
  37. 20193D Local Features for Direct Pairwise Registrationcited by 26
  38. 2020Learning to Orient Surfaces by Self-supervised Spherical CNNscited by 26

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 22; 2024: 16; 2025: 16; 2026: 4.

68 papers.

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  1. 2022Geometric Transformer for Fast and Robust Point Cloud Registrationcited by 200
  2. 2021Lepard: Learning partial point cloud matching in rigid and deformable scenescited by 85
  3. 20233D Registration with Maximal Cliquescited by 83
  4. 2023GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformercited by 83
  5. 2023Rotation-Invariant Transformer for Point Cloud Matchingcited by 68
  6. 2023Robust Outlier Rejection for 3D Registration with Variational Bayescited by 31
  7. 2023FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimatorscited by 29
  8. 2024MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Imagescited by 28
  9. 2022RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registrationcited by 25
  10. 2024PARE-Net: Position-Aware Rotation-Equivariant Networks for Robust Point Cloud Registrationcited by 18
  11. 2022ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairscited by 17
  12. 2023Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweightingcited by 17
38 more
  1. 2023Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environmentscited by 15
  2. 2023Generalizing Neural Human Fitting to Unseen Poses With Articulated SE(3) Equivariancecited by 14
  3. 2023PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registrationcited by 14
  4. 2025Rectified Point Flow: Generic Point Cloud Pose Estimationcited by 13
  5. 2025TurboReg: TurboClique for Robust and Efficient Point Cloud Registrationcited by 12
  6. 2021KTNet: Knowledge Transfer for Unpaired 3D Shape Completioncited by 9
  7. 2026CMHANet: A Cross-Modal Hybrid Attention Network for Point Cloud Registrationcited by 9
  8. 2025Register Any Point: Scaling 3D Point Cloud Registration by Flow Matchingcited by 8
  9. 2023Point Cloud Registration for LiDAR and Photogrammetric Data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithmscited by 7
  10. 2023Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimationcited by 7
  11. 2022UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registrationcited by 6
  12. 2022PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registrationcited by 6
  13. 2023ZeroReg: Zero-Shot Point Cloud Registration with Foundation Modelscited by 6
  14. 2024PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformercited by 6
  15. 2021COTReg:Coupled Optimal Transport based Point Cloud Registrationcited by 5
  16. 2023Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration with Correspondencescited by 5
  17. 2025RARE: Refine Any Registration of Pairwise Point Clouds via Zero-Shot Learningcited by 5
  18. 2026IGASA: Integrated Geometry-Aware and Skip-Attention Modules for Enhanced Point Cloud Registrationcited by 5
  19. 2023KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mappingcited by 4
  20. 2023Adaptive Multi-step Refinement Network for Robust Point Cloud Registrationcited by 4
  21. 2024MaskLRF: Self-supervised Pretraining via Masked Autoencoding of Local Reference Frames for Rotation-invariant 3D Point Set Analysiscited by 4
  22. 2024PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Trainingcited by 4
  23. 2024A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registrationcited by 4
  24. 2025Distilling 3D distinctive local descriptors for 6D pose estimationcited by 4
  25. 2023Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learningcited by 3
  26. 2023Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Priorcited by 3
  27. 2024ML-SemReg: Boosting Point Cloud Registration with Multi-level Semantic Consistencycited by 3
  28. 2024UMERegRobust - Universal Manifold Embedding Compatible Features for Robust Point Cloud Registrationcited by 3
  29. 2025Fully-Geometric Cross-Attention for Point Cloud Registrationcited by 3
  30. 2025FUSER: Feed-Forward MUltiview 3D Registration Transformer and SE(3)N Diffusion Refinementcited by 3
  31. 2022Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Trackingcited by 2
  32. 2023Graph Matching Optimization Network for Point Cloud Registrationcited by 2
  33. 2023SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registrationcited by 2
  34. 2024L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registrationcited by 2
  35. 2024Incremental Multiview Point Cloud Registration with Two-stage Candidate Retrievalcited by 2
  36. 2024Current Symmetry Group Equivariant Convolution Frameworks for Representation Learningcited by 2
  37. 2024Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registrationcited by 2
  38. 20243D Registration in 30 Years: A Surveycited by 2

50 most cited of 68 All 68 (JSON)