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Graph matching aims to establish correspondences between vertices of graphssuch that both the node and edge attributes agree. Various learning-basedmethods were recently proposed for finding correspondences between image keypoints based on deep graph matching formulations. While these approaches mainlyfocus on learning node and edge attributes, they completely ignore the 3Dgeometry of the underlying 3D objects depicted in the 2D images. We fill thisgap by proposing a trainable framework that takes advantage of graph neuralnetworks for learning a deformable 3D geometry model from inhomogeneous imagecollections, i.e.,~a set of images that depict different instances of objectsfrom the same category. Experimentally, we demonstrate that our methodoutperforms recent learning-based approaches for graph matching consideringboth accuracy and cycle-consistency error, while we in addition obtain theunderlying 3D geometry of the objects depicted in the 2D images.

Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections