  Many applications, such as autonomous driving, heavily rely on multi-modal
data where spatial alignment between the modalities is required. Most
multi-modal registration methods struggle computing the spatial correspondence
between the images using prevalent cross-modality similarity measures. In this
work, we bypass the difficulties of developing cross-modality similarity
measures, by training an image-to-image translation network on the two input
modalities. This learned translation allows training the registration network
using simple and reliable mono-modality metrics. We perform multi-modal
registration using two networks - a spatial transformation network and a
translation network. We show that by encouraging our translation network to be
geometry preserving, we manage to train an accurate spatial transformation
network. Compared to state-of-the-art multi-modal methods our presented method
is unsupervised, requiring no pairs of aligned modalities for training, and can
be adapted to any pair of modalities. We evaluate our method quantitatively and
qualitatively on commercial datasets, showing that it performs well on several
modalities and achieves accurate alignment.
