WGLSM: An end-to-end line matching network based on graph convolution

Line matching plays an essential role in Structure from Motion (SFM) and Simultaneous Localization and Mapping (SLAM), especially in low-texture scenes, where feature points are hard to be detected. In this paper, we present a new method by combining Convolutional Neural Networks and Graph Convolutional Networks to match line segments in pairs of images. We design a graph-based method to predict the assignment matrix of two feature sets with solving a relaxed optimal transport problem. In contrast to handcrafted line matching algorithms, our approach learns the line segment features and performs matching simultaneously through end-to-end weakly supervised training. The experiment results show that our method outperforms the state-of-the-art techniques and is robust to various image transformations. Besides, the generalization experiment illustrates that our method has good generalization ability without fine-tuning. The code of our work is available at https://github.com/mameng1/GraphLineMatching.