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Registration is a transformation estimation problem between two point clouds,which has a unique and critical role in numerous computer vision applications.The developments of optimization-based methods and deep learning methods haveimproved registration robustness and efficiency. Recently, the combinations ofoptimization-based and deep learning methods have further improved performance.However, the connections between optimization-based and deep learning methodsare still unclear. Moreover, with the recent development of 3D sensors and 3Dreconstruction techniques, a new research direction emerges to aligncross-source point clouds. This survey conducts a comprehensive survey,including both same-source and cross-source registration methods, and summarizethe connections between optimization-based and deep learning methods, toprovide further research insight. This survey also builds a new benchmark toevaluate the state-of-the-art registration algorithms in solving cross-sourcechallenges. Besides, this survey summarizes the benchmark data sets anddiscusses point cloud registration applications across various domains.Finally, this survey proposes potential research directions in this rapidlygrowing field.

A comprehensive survey on point cloud registration