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.

中文翻译：

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点云注册综合调查

配准是两点云之间的转换估计问题，从优化方面来看，这已经经历了很长的发展历史。深度学习的最新成功极大地提高了注册的鲁棒性和效率。该调查试图进行全面的审查，并建立基于优化的方法与深度学习方法之间的联系，以提供进一步的研究见解。此外，随着3D传感器和3D重建技术的最新发展，对准交叉源点云的新研究方向也应运而生。这项调查回顾了跨源点云注册的发展，并建立了一个新的基准来评估最新的注册算法。除了，该调查总结了基准数据集，并讨论了各个领域中的点云注册应用程序。最后，这项调查提出了在这个快速发展的领域中潜在的研究方向。