Rigid pairwise 3D point cloud registration: A survey

Over the past years, 3D point cloud registration has attracted unprecedented attention. Researchers develop various approaches to tackle the challenging task, such as optimization-based and deep learning-based methods. To systematically sort out the relevant literature and follow the state-of-the-art solutions, this paper conducts a thorough survey. We propose a novel taxonomy dubbed Intermediates Based Taxon (IBTaxon) which effectively categorizes multifarious registration approaches by the introduced intermediate variables or the leveraged intermediate modules. We further delve into each of the categories and present a comprehensive technique review with a focus on the distinct insight behind each of the methods. Besides, the relevant datasets and evaluation metrics are also combed and reorganized. We conclude our paper by discussing the possible open research problems and presenting our visions for future research in the field of 3D point cloud registration.