  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.
