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：线段是点的有力补充特征，能提供结构线索，对剧烈视角和光照变化具有鲁棒性，且可存在于无纹理区域。然而，由于部分遮挡、纹理缺失或重复性，线段的描述与匹配相比点更具挑战性。本文提出一种全新匹配范式，将点、线及其描述符统一至单一线框结构中。我们提出 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增强匹配的粘合度。除联合匹配带来的效率提升外，我们更证明在单一架构中利用这两种特征的互补性可显著提升性能。实验表明，在多种数据集与任务中，我们的匹配策略优于独立匹配线段和点的最先进方法。代码开源于 https://github.com/cvg/GlueStick。