  Registration is the process that computes the transformation that aligns sets
of data. Commonly, a registration process can be divided into four main steps:
target selection, feature extraction, feature matching, and transform
computation for the alignment. The accuracy of the result depends on multiple
factors, the most significant are the quantity of input data, the presence of
noise, outliers and occlusions, the quality of the extracted features,
real-time requirements and the type of transformation, especially those ones
defined by multiple parameters, like non-rigid deformations. Recent
advancements in machine learning could be a turning point in these issues,
particularly with the development of deep learning (DL) techniques, which are
helping to improve multiple computer vision problems through an abstract
understanding of the input data. In this paper, a review of deep learning-based
registration methods is presented. We classify the different papers proposing a
framework extracted from the traditional registration pipeline to analyse the
new learning-based proposal strengths. Deep Registration Networks (DRNs) try to
solve the alignment task either replacing part of the traditional pipeline with
a network or fully solving the registration problem. The main conclusions
extracted are, on the one hand, 1) learning-based registration techniques
cannot always be clearly classified in the traditional pipeline. 2) These
approaches allow more complex inputs like conceptual models as well as the
traditional 3D datasets. 3) In spite of the generality of learning, the current
proposals are still ad hoc solutions. Finally, 4) this is a young topic that
still requires a large effort to reach general solutions able to cope with the
problems that affect traditional approaches.
