A comprehensive survey on point cloud registration
Registration is a problem of transformation estimation between two point
clouds, which has experienced a long history of development from an
optimization aspect. The recent success of deep learning has vastly improved
registration robustness and efficiency. This survey tries to conduct a
comprehensive review and build the connection between optimization-based
methods and deep learning methods, to provide further research insight.
Moreover, with the recent development of 3D sensors and 3D reconstruction
techniques, a new research direction also emerges to align cross-source point
clouds. This survey reviews the development of cross-source point cloud
registration and builds a new benchmark to evaluate the state-of-the-art
registration algorithms. Besides, this survey summarizes the benchmark data
sets and discusses point cloud registration applications across various
domains. Finally, this survey proposes potential research directions in this
rapidly growing field.