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Image feature matching is a fundamental part of many geometric computervision applications, and using multiple images can improve performance. In thiswork, we formulate multi-image matching as a graph embedding problem then use aGraph Convolutional Network to learn an appropriate embedding function foraligning image features. We use cycle consistency to train our network in anunsupervised fashion, since ground truth correspondence is difficult orexpensive to aquire. In addition, geometric consistency losses can be added attraining time, even if the information is not available in the test set, unlikeprevious approaches that optimize cycle consistency directly. To the best ofour knowledge, no other works have used learning for multi-image featurematching. Our experiments show that our method is competitive with otheroptimization based approaches.

All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks