论文标题
OpenGlue：图像匹配的开源图神经网管道
OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching
论文作者
论文摘要
我们提出了OpenGlue：图像匹配的免费开源框架，该框架使用了受superglue \ cite {sarlin20superglue}的基于图神经网络的匹配器。我们表明，包括其他几何信息，例如本地特征量表，方向和仿射几何（例如，对于筛选功能），可以显着提高OpenGlue Matcher的性能。我们研究各种注意机制对准确性和速度的影响。我们还通过将本地描述符与上下文感知的描述符相结合，提出了一个简单的架构改进。公开可用的代码和针对不同本地功能的OpenGlue模型。
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.
