Point-Line Joint Matching Algorithm Based on Graph Neural Networks

Image matching faces challenges in maintaining robustness under complex conditions such as sparse textural regions, scenes with repetitive structural patterns, and large viewpoint variations, where neither point-based nor line-based feature matching alone can achieve consistent performance across all scenarios.This paper presents a novel graph neural network-based point-line joint matching algorithm designed to overcome these limitations. First, we introduce a connection matrix representation that unifies point and line features within a graph structure, eliminating the need for expensive line descriptor extraction while preserving geometric relationships. Second, we design a dynamic graph neural network incorporating self-attention, linear propagation, and cross-attention mechanisms to capture both intra-image and inter-image feature associations, enhancing global consistency and discriminative power. Third, we employ a Dual-Softmax matching strategy combined with negative log-likelihood loss to achieve efficient and robust matching even in repetitive structural scenarios. Extensive evaluations on the Wireframe dataset confirm our method's superiority, achieving 35.3% AUC-RANSAC@1px for point matching and 0.60 mAP for line matching, while maintaining real-time efficiency.Finally, we evaluate our method on the task of aircraft pose estimation.