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. •A novel taxonomy dubbed IBTaxon is proposed which categorizes registration methods as one-stage and two-stage approaches.•Following the IBTaxon, alternate and sequential optimization are induced as two strategies for two-stage approaches to achieve alignment.•Several widely used datasets and metrics for the standard evaluation and comparison of various registration methods are combed.•Experimental performances of several representative methods have been arranged and analyzed to provide a reference.•We make the conclusion that the balance between accuracy, speed, and robustness is more considerable rather than aspiring to a single indicator.