Line segments are powerful features complementary to points. They offer structural cues, robust to drastic viewpoint and illumination changes, and can be present even in texture-less areas. However, describing and matching them is more challenging compared to points due to partial occlusions, lack of texture, or repetitiveness. This paper introduces a new matching paradigm, where points, lines, and their descriptors are unified into a single wireframe structure. We propose GlueStick, a deep matching Graph Neural Network (GNN) that takes two wireframes from different images and leverages the connectivity information between nodes to better glue them together. In addition to the increased efficiency brought by the joint matching, we also demonstrate a large boost of performance when leveraging the complementary nature of these two features in a single architecture. We show that our matching strategy outperforms the state-of-the-art approaches independently matching line segments and points for a wide variety of datasets and tasks. The code is available at https://github.com/cvg/GlueStick.

翻译：线段是比点更具表现力的互补特征。它们能提供结构线索，对剧烈视角变化和光照变化具有鲁棒性，即使在纹理缺失区域也能存在。然而，由于局部遮挡、纹理缺失或重复性，线段的描述与匹配相比点更具挑战性。本文提出一种新的匹配范式，将点、线及其描述子统一为单一线框结构。我们提出了GlueStick——一种深度匹配图神经网络（GNN），它接收来自不同图像的两个线框，并利用节点间的连接信息更好地将它们粘合在一起。除了联合匹配带来的效率提升外，我们还证明了在单一架构中利用这两种特征的互补性能够显著提升性能。实验表明，在多种数据集和任务上，我们的匹配策略在独立匹配线段和点的方法中均优于现有最先进技术。代码已开源：https://github.com/cvg/GlueStick。