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：近年来，计算机视觉与深度学习的进展在估计复杂物体与场景的未配准点云之间的刚体/相似变换方面展现出良好性能。然而，这些方法的性能大多基于单一传感器（如Kinect或RealSense相机）的小规模数据集进行评估，缺乏对它们在摄影测量三维制图场景中适用性的全面认识。本文对当前最先进（SOTA）的点云配准方法进行综合评述，利用涵盖室内至卫星源的多样化点云数据集分析与评估这些方法。定量分析揭示了这些方法的优势、适用性、挑战与未来趋势。与现有将点云配准视为整体过程的分析工作不同，本实验分析基于其固有的两阶段流程——包括基于特征/关键点的初始粗配准以及通过云对云（C2C）优化实现的密集精配准，从而更深入理解各类方法。我们测试了超过十种方法，涵盖经典手工构建、基于深度学习的特征对应以及鲁棒C2C方法。实验发现，在测试数据集上，大多数算法的成功率低于40%，且现有算法在三维稀疏对应搜索、处理复杂几何与遮挡点云的能力方面仍存在显著提升空间。基于三个数据集的评估统计结果，我们总结了各步骤的最佳性能方法，并提出建议与未来工作展望。