  Multi-modal registration is a required step for many image-guided procedures,
especially ultrasound-guided interventions that require anatomical context.
While a number of such registration algorithms are already available, they all
require a good initialization to succeed due to the challenging appearance of
ultrasound images and the arbitrary coordinate system they are acquired in. In
this paper, we present a novel approach to solve the problem of registration of
an ultrasound sweep to a pre-operative image. We learn dense keypoint
descriptors from which we then estimate the registration. We show that our
method overcomes the challenges inherent to registration tasks with freehand
ultrasound sweeps, namely, the multi-modality and multidimensionality of the
data in addition to lack of precise ground truth and low amounts of training
examples. We derive a registration method that is fast, generic, fully
automatic, does not require any initialization and can naturally generate
visualizations aiding interpretability and explainability. Our approach is
evaluated on a clinical dataset of paired MR volumes and ultrasound sequences.
