mlsanityData to 7 Oct 2026

Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration

arXiv 1605.03344 ↗First posted 11 May 2016cs.CVcited by 159

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Abstract (arXiv)

The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the L2 error metric defined in ICP. The Go-ICP method is based on a branch-and-bound (BnB) scheme that searches the entire 3D motion space SE(3). By exploiting the special structure of SE(3) geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.

Key facts. Cited by 159 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2016.

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Citing papers per year: 2021: 35; 2022: 27; 2023: 23; 2024: 31; 2025: 21; 2026: 22.

159 papers.

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  1. 2021Robust Point Cloud Registration Framework Based on Deep Graph Matchingcited by 96
  2. 2021Lepard: Learning partial point cloud matching in rigid and deformable scenescited by 85
  3. 2021A comprehensive survey on point cloud registrationcited by 84
  4. 20233D Registration with Maximal Cliquescited by 83
  5. 2021NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Gocited by 81
  6. 2022FS6D: Few-Shot 6D Pose Estimation of Novel Objectscited by 81
  7. 2022Full-Body Articulated Human-Object Interactioncited by 76
  8. 2021You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptorscited by 68
  9. 2021LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAMcited by 67
  10. 2021Deep Hough Voting for Robust Global Registrationcited by 53
  11. 2021HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registrationcited by 47
  12. 2021Accurate Point Cloud Registration with Robust Optimal Transportcited by 38
38 more
  1. 20233D Face Reconstruction with the Geometric Guidance of Facial Part Segmentationcited by 38
  2. 2022Globally-Optimal Contrast Maximisation for Event Camerascited by 31
  3. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  4. 2021MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Squarecited by 27
  5. 2021R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Methodcited by 26
  6. 2021SSC: Semantic Scan Context for Large-Scale Place Recognitioncited by 24
  7. 2021ReAgent: Point Cloud Registration using Imitation and Reinforcement Learningcited by 23
  8. 2021Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimizationcited by 21
  9. 2021PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Cloudscited by 20
  10. 2022A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environmentscited by 19
  11. 2024FastMAC: Stochastic Spectral Sampling of Correspondence Graphcited by 16
  12. 2024Scalable 3D Registration via Truncated Entry-wise Absolute Residualscited by 16
  13. 2024KISS-Matcher: Fast and Robust Point Cloud Registration Revisitedcited by 16
  14. 2022ARCS: Accurate Rotation and Correspondence Searchcited by 15
  15. 2022DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimizationcited by 14
  16. 2023Accelerating Globally Optimal Consensus Maximization in Geometric Visioncited by 14
  17. 2024Multiway Point Cloud Mosaicking with Diffusion and Global Optimizationcited by 14
  18. 2022MatchNorm: Learning-based Point Cloud Registration for 6D Object Pose Estimation in the Real Worldcited by 13
  19. 2022Certifiable 3D Object Pose Estimation: Foundations, Learning Models, and Self-Trainingcited by 12
  20. 2023Rearrangement Planning for General Part Assemblycited by 12
  21. 2026Advances in Global Solvers for 3D Visioncited by 12
  22. 20213D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learningcited by 11
  23. 2023G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Modelcited by 11
  24. 2022Self-Supervised Point Cloud Registration with Deep Versatile Descriptorscited by 10
  25. 2022Overlap-guided Gaussian Mixture Models for Point Cloud Registrationcited by 10
  26. 2022Coarse-to-Fine Point Cloud Registration with SE(3)-Equivariant Representationscited by 9
  27. 2026CMHANet: A Cross-Modal Hybrid Attention Network for Point Cloud Registrationcited by 9
  28. 2021A Robust Loss for Point Cloud Registrationcited by 8
  29. 2022VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registrationcited by 8
  30. 2024Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Searchcited by 8
  31. 2024A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Partscited by 8
  32. 2025BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenescited by 8
  33. 2021WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registrationcited by 7
  34. 2022Zero NeRF: Registration with Zero Overlapcited by 7
  35. 2024RANSAC Back to SOTA: A Two-stage Consensus Filtering for Real-time 3D Registrationcited by 7
  36. 2021Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interactioncited by 6
  37. 2023Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlapcited by 6
  38. 2024Tight Fusion of Events and Inertial Measurements for Direct Velocity Estimationcited by 6

50 most cited of 159 All 159 (JSON)