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
this https URL

本文提出一种新的匹配模式，将点、线及其描述符统一成一个单独的框架结构，并提出 GlueStick，一种深度匹配图神经网络，以便从不同的图像中取出两个线框并利用节点之间的连接信息更好地将它们粘合在一起。我们证明了这种匹配策略可以优于当前最先进的独立匹配线段和点的方法，适用于各种数据集和任务。