  We address the problem of registering synchronized color (RGB) and
multi-spectral (MS) images featuring very different resolution by solving
stereo matching correspondences. Purposely, we introduce a novel RGB-MS dataset
framing 13 different scenes in indoor environments and providing a total of 34
image pairs annotated with semi-dense, high-resolution ground-truth labels in
the form of disparity maps. To tackle the task, we propose a deep learning
architecture trained in a self-supervised manner by exploiting a further RGB
camera, required only during training data acquisition. In this setup, we can
conveniently learn cross-modal matching in the absence of ground-truth labels
by distilling knowledge from an easier RGB-RGB matching task based on a
collection of about 11K unlabeled image triplets. Experiments show that the
proposed pipeline sets a good performance bar (1.16 pixels average registration
error) for future research on this novel, challenging task.
