A Detector-Oblivious Multi-Arm Network for Keypoint Matching

Xuelun Shen, Qian Hu, Xin Li, Cheng Wang

Abstract: This paper presents a matching network to establish point correspondence between images. We propose a Multi-Arm Network (MAN) capable of learning region overlap and depth, which can greatly improve keypoint matching robustness while bringing an extra 50% of computational time during the inference stage. By adopting a different design from the state-of-the-art learning based pipeline SuperGlue framework, which requires retraining when a different keypoint detector is adopted, our network can directly work with different keypoint detectors without time-consuming retraining processes. Comprehensive experiments conducted on four public benchmarks involving both outdoor and indoor scenarios demonstrate that our proposed MAN outperforms state-of-the-art methods.