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Many applications, such as autonomous driving, heavily rely on multi-modaldata where spatial alignment between the modalities is required. Mostmulti-modal registration methods struggle computing the spatial correspondencebetween the images using prevalent cross-modality similarity measures. In thiswork, we bypass the difficulties of developing cross-modality similaritymeasures, by training an image-to-image translation network on the two inputmodalities. This learned translation allows training the registration networkusing simple and reliable mono-modality metrics. We perform multi-modalregistration using two networks - a spatial transformation network and atranslation network. We show that by encouraging our translation network to begeometry preserving, we manage to train an accurate spatial transformationnetwork. Compared to state-of-the-art multi-modal methods our presented methodis unsupervised, requiring no pairs of aligned modalities for training, and canbe adapted to any pair of modalities. We evaluate our method quantitatively andqualitatively on commercial datasets, showing that it performs well on severalmodalities and achieves accurate alignment.

Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation