Feng, HuihangLiu, LupengXiao, JunChaine, RaphaëlleDeng, ZhigangKim, Min H.2023-10-092023-10-092023978-3-03868-234-9https://doi.org/10.2312/pg.20231285https://diglib.eg.org:443/handle/10.2312/pg20231285This paper presents a progressive graph matching network shorted as PGMNet. The method is more explainable and can match features from easy to hard. PGMNet contains two major blocks: sinkformers module and guided attention module. First, we use sinkformers to get the similar matrix which can be seen as an assignment matrix between two sets of feature keypoints. Matches with highest scores in both rows and columns are selected as pre-matched correspondences. These pre-matched matches can be leveraged to guide the update and matching of ambiguous features. The matching quality can be progressively improved as the the transformer blocks go deeper as visualized in Figure 1. Experiments show that our method achieves better results with typical attention-based methods.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> Matching; Mixed / augmented realityComputing methodologiesMatchingMixed / augmented reality10.2312/pg.20231285119-1202 pages