3D Registration in 30 Years: A Survey
Jiaqi Yang, Chu'ai Zhang, Zhengbao Wang, Xinyue Cao, Xuan Ouyang, Xiyu, Zhang, Zhenxuan Zeng, Zhao Zeng, Borui Lu, Zhiyi Xia, Qian Zhang, Yulan Guo, and Yanning Zhang

TL;DR
This survey comprehensively reviews 30 years of 3D point cloud registration research, covering various sub-areas, datasets, metrics, and future directions, providing a valuable resource for researchers in related fields.
Contribution
It offers an extensive, up-to-date overview of 3D registration methods, taxonomy, and evaluations, filling gaps left by previous limited surveys.
Findings
Detailed taxonomy of registration methods
Analysis of datasets and evaluation metrics
Insights into future research directions
Abstract
3D point cloud registration is a fundamental problem in computer vision, computer graphics, robotics, remote sensing, and etc. Over the last thirty years, we have witnessed the amazing advancement in this area with numerous kinds of solutions. Although a handful of relevant surveys have been conducted, their coverage is still limited. In this work, we present a comprehensive survey on 3D point cloud registration, covering a set of sub-areas such as pairwise coarse registration, pairwise fine registration, multi-view registration, cross-scale registration, and multi-instance registration. The datasets, evaluation metrics, method taxonomy, discussions of the merits and demerits, insightful thoughts of future directions are comprehensively presented in this survey. The regularly updated project page of the survey is available at…
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Taxonomy
Topics3D Shape Modeling and Analysis · Image Processing and 3D Reconstruction · Remote Sensing and LiDAR Applications
MethodsSparse Evolutionary Training
