系统梳理30年三维点云配准发展脉络，覆盖六大核心方向。
3D Registration in 30 Years: A Survey
- 按任务类型分类，涵盖粗配准、精配准等六大子领域。
- 总结主流数据集与评估指标，分析各方法优缺点。
- 适合从事三维视觉、机器人定位的研究者参考。
三维点云配准是计算机视觉、计算机图形学、机器人学、遥感等领域中的基础问题。过去三十年间，该领域涌现出大量解决方案，取得了显著进展。尽管已有少数相关综述，但覆盖面仍有限。本文全面回顾了三维点云配准研究，涵盖成对粗配准、成对精配准、多视图配准、跨尺度配准及多实例配准等多个子方向。系统梳理了常用数据集、评估指标、方法分类体系，并深入讨论各类方法的优劣，提出未来发展方向的见解。本综述的持续更新项目页面见：https://github.com/Amyyyy11/3D-Registration-in-30-Years-A-Survey。
原文摘要 · Abstract (English)
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 https://github.com/Amyyyy11/3D-Registration-in-30-Years-A-Survey.
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