# Pairwise Point Cloud Registration using Graph Matching and Rotation-invariant Features

arXiv: https://arxiv.org/abs/2105.02151 · HTML: https://mlsanity.com/paper/2105.02151/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 5 May 2021.

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

- [A Functional Representation for Graph Matching](https://mlsanity.com/paper/1901.05179/) (2019)
- [Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration](https://mlsanity.com/paper/1808.03899/) (2018)

## Built on by (most cited first, 2 of 2)

- [GlobalMatch: Registration of Forest Terrestrial Point Clouds by Global Matching of Relative Stem Positions](https://mlsanity.com/paper/2112.11121/) (2021)
- [Spectral Consistency-Guided Multiview Point Cloud Registration for Low-Overlap Scenes](https://mlsanity.com/paper/2609.12417/) (2026)

## Abstract (arXiv)

Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds. However, the finding of reliable correspondence relies on establishing a robust and discriminative description of elements and the correct matching of corresponding elements. In this letter, we develop a coarse-to-fine registration strategy, which utilizes rotation-invariant features and a new weighted graph matching method for iteratively finding correspondence. In the graph matching method, the similarity of nodes and edges in Euclidean and feature space are formulated to construct the optimization function. The proposed strategy is evaluated using two benchmark datasets and compared with several state-of-the-art methods. Regarding the experimental results, our proposed method can achieve a fine registration with rotation errors of less than 0.2 degrees and translation errors of less than 0.1m.