# Fast Simultaneous Gravitational Alignment of Multiple Point Sets

arXiv: https://arxiv.org/abs/2106.11308 · HTML: https://mlsanity.com/paper/2106.11308/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [3DRegNet: A Deep Neural Network for 3D Point Registration](https://mlsanity.com/paper/1904.01701/) (2019)
- [Learning multiview 3D point cloud registration](https://mlsanity.com/paper/2001.05119/) (2020)
- [A Quantum Computational Approach to Correspondence Problems on Point Sets](https://mlsanity.com/paper/1912.12296/) (2019)
- [An Adaptive Data Representation for Robust Point-Set Registration and Merging](https://mlsanity.com/paper/1511.04240/) (2015)
- [Density Adaptive Point Set Registration](https://mlsanity.com/paper/1804.01495/) (2018)

## Abstract (arXiv)

The problem of simultaneous rigid alignment of multiple unordered point sets which is unbiased towards any of the inputs has recently attracted increasing interest, and several reliable methods have been newly proposed. While being remarkably robust towards noise and clustered outliers, current approaches require sophisticated initialisation schemes and do not scale well to large point sets. This paper proposes a new resilient technique for simultaneous registration of multiple point sets by interpreting the latter as particle swarms rigidly moving in the mutually induced force fields. Thanks to the improved simulation with altered physical laws and acceleration of globally multiply-linked point interactions with a 2^D-tree (D is the space dimensionality), our Multi-Body Gravitational Approach (MBGA) is robust to noise and missing data while supporting more massive point sets than previous methods (with 10^5 points and more). In various experimental settings, MBGA is shown to outperform several baseline point set alignment approaches in terms of accuracy and runtime. We make our source code available for the community to facilitate the reproducibility of the results.