# Registration of multi-view point sets under the perspective of expectation-maximization

arXiv: https://arxiv.org/abs/2002.07464 · HTML: https://mlsanity.com/paper/2002.07464/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

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

- [LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration](https://mlsanity.com/paper/2103.15039/) (2021)
- [Robust Multi-view Registration of Point Sets with Laplacian Mixture Model](https://mlsanity.com/paper/2110.13744/) (2021)
- [Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism](https://mlsanity.com/paper/2205.02996/) (2022)
- [3D Registration in 30 Years: A Survey](https://mlsanity.com/paper/2412.13735/) (2024)
- [3DMNDT:3D multi-view registration method based on the normal distributions transform](https://mlsanity.com/paper/2103.11084/) (2021)
- [Multiview Point Cloud Registration via Optimization in an Autoencoder Latent Space](https://mlsanity.com/paper/2504.21467/) (2025)
- [Multiview point cloud registration with anisotropic and space-varying localization noise](https://mlsanity.com/paper/2201.00708/) (2022)
- [Robust Motion Averaging for Multi-view Registration of Point Sets Based Maximum Correntropy Criterion](https://mlsanity.com/paper/2208.11327/) (2022)

## Abstract (arXiv)

Registration of multi-view point sets is a prerequisite for 3D model reconstruction. To solve this problem, most of previous approaches either partially explore available information or blindly utilize unnecessary information to align each point set, which may lead to the undesired results or introduce extra computation complexity. To this end, this paper consider the multi-view registration problem as a maximum likelihood estimation problem and proposes a novel multi-view registration approach under the perspective of Expectation-Maximization (EM). The basic idea of our approach is that different data points are generated by the same number of Gaussian mixture models (GMMs). For each data point in one point set, its nearest neighbors can be searched from other well-aligned point sets. Then, we can suppose this data point is generated by the special GMM, which is composed of each nearest neighbor adhered with one Gaussian distribution. Based on this assumption, it is reasonable to define the likelihood function including all rigid transformations, which requires to be estimated for multi-view registration. Subsequently, the EM algorithm is utilized to maximize the likelihood function so as to estimate all rigid transformations. Finally, the proposed approach is tested on several bench mark data sets and compared with some state-of-the-art algorithms. Experimental results illustrate its super performance on accuracy, robustness and efficiency for the registration of multi-view point sets.