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First, we propose a convolutional neural network architecture\nfor geometric matching. The architecture is based on three main components that\nmimic the standard steps of feature extraction, matching and simultaneous\ninlier detection and model parameter estimation, while being trainable\nend-to-end. Second, we demonstrate that the network parameters can be trained\nfrom synthetically generated imagery without the need for manual annotation and\nthat our matching layer significantly increases generalization capabilities to\nnever seen before images. 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