# Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement

arXiv: https://arxiv.org/abs/1903.04630 · HTML: https://mlsanity.com/paper/1903.04630/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [A comprehensive survey on point cloud registration](https://mlsanity.com/paper/2103.02690/) (2021)
- [IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration](https://mlsanity.com/paper/2111.09624/) (2021)
- [KSS-ICP: Point Cloud Registration based on Kendall Shape Space](https://mlsanity.com/paper/2211.02807/) (2022)
- [Cross-source Point Cloud Registration: Challenges, Progress and Prospects](https://mlsanity.com/paper/2305.13570/) (2023)
- [GMF: General Multimodal Fusion Framework for Correspondence Outlier Rejection](https://mlsanity.com/paper/2211.00207/) (2022)
- [FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization](https://mlsanity.com/paper/2309.08966/) (2023)
- [HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion](https://mlsanity.com/paper/2304.04508/) (2023)
- [Cross3DReg: Towards a Large-scale Real-world Cross-source Point Cloud Registration Benchmark](https://mlsanity.com/paper/2509.06456/) (2025)

## Abstract (arXiv)

Many types of 3D acquisition sensors have emerged in recent years and point cloud has been widely used in many areas. Accurate and fast registration of cross-source 3D point clouds from different sensors is an emerged research problem in computer vision. This problem is extremely challenging because cross-source point clouds contain a mixture of various variances, such as density, partial overlap, large noise and outliers, viewpoint changing. In this paper, an algorithm is proposed to align cross-source point clouds with both high accuracy and high efficiency. There are two main contributions: firstly, two components, the weak region affinity and pixel-wise refinement, are proposed to maintain the global and local information of 3D point clouds. Then, these two components are integrated into an iterative tensor-based registration algorithm to solve the cross-source point cloud registration problem. We conduct experiments on synthetic cross-source benchmark dataset and real cross-source datasets. Comparison with six state-of-the-art methods, the proposed method obtains both higher efficiency and accuracy.