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

arXiv: https://arxiv.org/abs/2305.13570 · HTML: https://mlsanity.com/paper/2305.13570/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 16 of 16)

- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [RPM-Net: Robust Point Matching using Learned Features](https://mlsanity.com/paper/2003.13479/) (2020)
- [D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features](https://mlsanity.com/paper/2003.03164/) (2020)
- [SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration](https://mlsanity.com/paper/2011.12149/) (2020)
- [PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency](https://mlsanity.com/paper/2103.05465/) (2021)
- [Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences](https://mlsanity.com/paper/2005.01014/) (2020)
- [DeepGMR: Learning Latent Gaussian Mixture Models for Registration](https://mlsanity.com/paper/2008.09088/) (2020)
- [IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration](https://mlsanity.com/paper/2111.09624/) (2021)
- [A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures](https://mlsanity.com/paper/1608.05143/) (2016)
- [A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds](https://mlsanity.com/paper/1610.07324/) (2016)
- [Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement](https://mlsanity.com/paper/1903.04630/) (2019)
- [GMF: General Multimodal Fusion Framework for Correspondence Outlier Rejection](https://mlsanity.com/paper/2211.00207/) (2022)
- [A Graph-Matching Approach for Cross-view Registration of Over-view 2 and Street-view based Point Clouds](https://mlsanity.com/paper/2202.06857/) (2022)
- [Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism](https://mlsanity.com/paper/2205.02996/) (2022)

## Built on by (most cited first, 9 of 9)

- [FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization](https://mlsanity.com/paper/2309.08966/) (2023)
- [VRHCF: Cross-Source Point Cloud Registration via Voxel Representation and Hierarchical Correspondence Filtering](https://mlsanity.com/paper/2403.10085/) (2024)
- [DBDNet:Partial-to-Partial Point Cloud Registration with Dual Branches Decoupling](https://mlsanity.com/paper/2310.11733/) (2023)
- [Efficient and Distributed Large-Scale 3D Map Registration using Tomographic Features](https://mlsanity.com/paper/2406.19461/) (2024)
- [LPRnet: A self-supervised registration network for LiDAR and photogrammetric point clouds](https://mlsanity.com/paper/2501.05669/) (2025)
- [Object Affordance Recognition and Grounding via Multi-scale Cross-modal Representation Learning](https://mlsanity.com/paper/2508.01184/) (2025)
- [Advancing Precision in Multi-Point Cloud Fusion Environments](https://mlsanity.com/paper/2508.03179/) (2025)
- [V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems](https://mlsanity.com/paper/2407.10195/) (2024)
- [Confidence-Driven Facade Refinement of 3D Building Models Using MLS Point Clouds](https://mlsanity.com/paper/2604.03797/) (2026)

## 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.