# Robust affine point matching via quadratic assignment on Grassmannians

arXiv: https://arxiv.org/abs/2303.02698 · HTML: https://mlsanity.com/paper/2303.02698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [A comprehensive survey on point cloud registration](https://mlsanity.com/paper/2103.02690/) (2021)
- [Graph Matching: Relax at Your Own Risk](https://mlsanity.com/paper/1405.3133/) (2014)
- [A Grassmannian Graph Approach to Affine Invariant Feature Matching](https://mlsanity.com/paper/1601.07648/) (2016)
- [An approach to robust ICP initialization](https://mlsanity.com/paper/2212.05332/) (2022)

## Abstract (arXiv)

Robust Affine Matching with Grassmannians (RoAM) is a new algorithm to perform affine registration of point clouds. The algorithm is based on minimizing the Frobenius distance between two elements of the Grassmannian. For this purpose, an indefinite relaxation of the Quadratic Assignment Problem (QAP) is used, and several approaches to affine feature matching are studied and compared. Experiments demonstrate that RoAM is more robust to noise and point discrepancy than previous methods.