  Point cloud registration (PCR) involves determining a rigid transformation
that aligns one point cloud to another. Despite the plethora of outstanding
deep learning (DL)-based registration methods proposed, comprehensive and
systematic studies on DL-based PCR techniques are still lacking. In this paper,
we present a comprehensive survey and taxonomy of recently proposed PCR
methods. Firstly, we conduct a taxonomy of commonly utilized datasets and
evaluation metrics. Secondly, we classify the existing research into two main
categories: supervised and unsupervised registration, providing insights into
the core concepts of various influential PCR models. Finally, we highlight open
challenges and potential directions for future research. A curated collection
of valuable resources is made available at https://github.com/yxzhang15/PCR.
