We present OpenGlue: a free open-source framework for image matching, that uses a Graph Neural Network-based matcher inspired by SuperGlue \cite{sarlin20superglue}. We show that including additional geometrical information, such as local feature scale, orientation, and affine geometry, when available (e.g. for SIFT features), significantly improves the performance of the OpenGlue matcher. We study the influence of the various attention mechanisms on accuracy and speed. We also present a simple architectural improvement by combining local descriptors with context-aware descriptors. The code and pretrained OpenGlue models for the different local features are publicly available.

翻译：我们展示了 OpenGlue : 一个用于图像匹配的免费开放源码框架, 它使用由 SuperGlue\ cite{sarlin20superglue} 所启发的基于图形神经网络的匹配器。 我们显示, 包含额外的几何信息, 如本地地标比例、 方向和等近距几何信息( 如SIFT 特征), 大大改善了 OpenGlue 匹配器的性能。 我们研究了各种关注机制对准确性和速度的影响。 我们还展示了简单的建筑改进, 将本地描述器与上下文描述器相结合。 用于不同本地地标的代码和预先培训过的 OpenGlue 模型可以公开使用 。