Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or RealSense cameras), lacking a comprehensive overview of their applicability in photogrammetric 3D mapping scenarios. In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. The quantitative analysis allows for exploring the strengths, applicability, challenges, and future trends of these methods. In contrast to existing analysis works that introduce point cloud registration as a holistic process, our experimental analysis is based on its inherent two-step process to better comprehend these approaches including feature/keypoint-based initial coarse registration and dense fine registration through cloud-to-cloud (C2C) optimization. More than ten methods, including classic hand-crafted, deep-learning-based feature correspondence, and robust C2C methods were tested. We observed that the success rate of most of the algorithms are fewer than 40% over the datasets we tested and there are still are large margin of improvement upon existing algorithms concerning 3D sparse corresopondence search, and the ability to register point clouds with complex geometry and occlusions. With the evaluated statistics on three datasets, we conclude the best-performing methods for each step and provide our recommendations, and outlook future efforts.

翻译：近期计算机视觉与深度学习的进展在估计复杂物体与场景中未配准点云间的刚体/相似变换方面展现出良好性能。然而，这些算法的性能大多基于单一传感器（如Kinect或RealSense相机）的有限数据集进行评估，缺乏对其在摄影测量三维建图场景中适用性的全面概述。本文对最先进的点云配准方法进行了系统性综述，利用从室内到卫星源的多类点云数据对这些方法进行了分析与评估。定量分析揭示了这些方法的优势、适用性、挑战及未来趋势。与现有将点云配准视为整体过程的分析工作不同，我们的实验分析基于其固有的两步流程，以更深入理解包括基于特征/关键点的初始粗配准和通过点云间优化的密集精配准方法。我们测试了十余种方法，涵盖经典手工设计、基于深度学习的特征对应以及鲁棒的点云间方法。实验发现，在测试数据集上多数算法的成功率不足40%，且在三维稀疏对应搜索、处理具有复杂几何与遮挡的点云能力方面，现有算法仍存在较大改进空间。基于三个数据集的评估统计结果，我们总结了各步骤的最优方法，提出建议，并展望未来方向。