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We present OpenGlue: a free open-source framework for image matching, thatuses a Graph Neural Network-based matcher inspired by SuperGlue\cite{sarlin20superglue}. We show that including additional geometricalinformation, such as local feature scale, orientation, and affine geometry,when available (e.g. for SIFT features), significantly improves the performanceof the OpenGlue matcher. We study the influence of the various attentionmechanisms on accuracy and speed. We also present a simple architecturalimprovement by combining local descriptors with context-aware descriptors. Thecode and pretrained OpenGlue models for the different local features arepublicly available.

OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching