# GlobalMatch: Registration of Forest Terrestrial Point Clouds by Global Matching of Relative Stem Positions

arXiv: https://arxiv.org/abs/2112.11121 · HTML: https://mlsanity.com/paper/2112.11121/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 66 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Practical optimal registration of terrestrial LiDAR scan pairs](https://mlsanity.com/paper/1811.09962/) (2018)
- [Pairwise Point Cloud Registration using Graph Matching and Rotation-invariant Features](https://mlsanity.com/paper/2105.02151/) (2021)

## Built on by (most cited first, 1 of 1)

- [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://mlsanity.com/paper/2512.01850/) (2025)

## Abstract (arXiv)

Registering point clouds of forest environments is an essential prerequisite for LiDAR applications in precision forestry. State-of-the-art methods for forest point cloud registration require the extraction of individual tree attributes, and they have an efficiency bottleneck when dealing with point clouds of real-world forests with dense trees. We propose an automatic, robust, and efficient method for the registration of forest point clouds. Our approach first locates tree stems from raw point clouds and then matches the stems based on their relative spatial relationship to determine the registration transformation. The algorithm requires no extra individual tree attributes and has quadratic complexity to the number of trees in the environment, allowing it to align point clouds of large forest environments. Extensive experiments on forest terrestrial point clouds have revealed that our method inherits the effectiveness and robustness of the stem-based registration strategy while exceedingly increasing its efficiency. Besides, we introduce a new benchmark dataset that complements the very few existing open datasets for the development and evaluation of registration methods for forest point clouds. The source code of our method and the dataset are available at https://github.com/zexinyang/GlobalMatch.