mlsanityData to 7 Oct 2026

Cross-source Point Cloud Registration: Challenges, Progress and Prospects

arXiv 2305.13570 ↗First posted 23 May 2023cs.CVcited by 9

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Abstract (arXiv)

The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing attention with the fast development background of 3D sensor technologies. Different from the conventional same-source point clouds that focus on data from same kind of 3D sensor (e.g., Kinect), CSPCs come from different kinds of 3D sensors (e.g., Kinect and { LiDAR}). CSPC registration generalizes the requirement of data acquisition from same-source to different sources, which leads to generalized applications and combines the advantages of multiple sensors. In this paper, we provide a systematic review on CSPC registration. We first present the characteristics of CSPC, and then summarize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area and explain the role in several application fields.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

16 of 33 references.

References · 16

  1. 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 333
  2. 2020TEASER: Fast and Certifiable Point Cloud Registrationcited by 274
  3. 2020Deep Global Registrationcited by 228
  4. 2020RPM-Net: Robust Point Matching using Learned Featurescited by 181
  5. 2020D3Feat: Joint Learning of Dense Detection and Description of 3D Local Featurescited by 175
  6. 2021PointDSC: Robust Point Cloud Registration using Deep Spatial Consistencycited by 142
  7. 2020SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registrationcited by 141
  8. 2020Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondencescited by 108
  9. 2020DeepGMR: Learning Latent Gaussian Mixture Models for Registrationcited by 91
  10. 2021IMFNet: Interpretable Multimodal Fusion for Point Cloud Registrationcited by 12
  11. 2016A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structurescited by 11
  12. 2016A coarse-to-fine algorithm for registration in 3D street-view cross-source point cloudscited by 8
4 more
  1. 2019Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinementcited by 8
  2. 2022GMF: General Multimodal Fusion Framework for Correspondence Outlier Rejectioncited by 7
  3. 2022A Graph-Matching Approach for Cross-view Registration of Over-view 2 and Street-view based Point Cloudscited by 4
  4. 2022Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanismcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 1.

9 papers.

  1. 2023FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimizationcited by 3
  2. 2024VRHCF: Cross-Source Point Cloud Registration via Voxel Representation and Hierarchical Correspondence Filteringcited by 3
  3. 2023DBDNet:Partial-to-Partial Point Cloud Registration with Dual Branches Decouplingcited by 2
  4. 2024Efficient and Distributed Large-Scale 3D Map Registration using Tomographic Featurescited by 1
  5. 2025LPRnet: A self-supervised registration network for LiDAR and photogrammetric point cloudscited by 1
  6. 2025Object Affordance Recognition and Grounding via Multi-scale Cross-modal Representation Learningcited by 1
  7. 2025Advancing Precision in Multi-Point Cloud Fusion Environmentscited by 1
  8. 2024V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systemscited by 0
  9. 2026Confidence-Driven Facade Refinement of 3D Building Models Using MLS Point Cloudscited by 0