LTGNN: A Line‐Transformer Graph Neural Network for Robust Line Feature Matching

ABSTRACT Lines are prevalent geometric features in urban environments and serve as an important complement to point features, playing an irreplaceable role in specific scenarios and tasks. Line feature matching is thus a crucial and fundamental task in photogrammetry and computer vision. However, matching line features remains challenging, particularly in scenes with semantic ambiguity caused by dense fragmentation and similar structures. This study embeds a novel line transformation block into a graph neural network, leading to a novel network, that is, Line‐Transformer Graph Neural Network (LTGNN), which optimizes the global topology while generating geometrically enhanced semantic descriptors for robust line feature matching. Furthermore, the proposed LTGNN leverages the complementary nature between point and line features to integrate local and global information. Firstly, we construct a unified graph structure called an LSD‐Wireframe using the retrieved point and line features. Secondly, we implement position encoding for LSD‐Wireframe nodes, which is subsequently passed into the LTGNN for enhancing information propagation and facilitating the learning of spatial and visual relationships among nodes. Finally, we employ a geometry‐constrained hybrid loss function to supervise the training process and optimize the network toward producing accurate and consistent line correspondences. We evaluated the proposed LTGNN on three different datasets and compared it to six benchmarking methods. Experimental results demonstrate that the proposed LTGNN outperforms others in feature matching, homography estimation, and camera localization applications.