## 2 Related Work  
### Traditional Methods  
To expand the viability and effectiveness of traditional point cloud registration, many studies have proposed some methods to tackle these challenges, including coarse registration methods and fine registration methods.  
Coarse registration is a rapid estimation of an initial matrix without knowing any initial positions between the source and target point clouds. The course registration approach is always composed of four stages: keypoint detection, feature descriptor generation, and feature matching and transformation estimation. Among the preceding processes, generating feature descriptors is unquestionably the most significant, and numerous studies on feature descriptors, particularly local feature descriptors, have been presented for point cloud registration. [PERSON] and [PERSON] et al. [7] presented a spin image descriptor. Each keypoint described by a spin image is insensitive to rigid transformation, and keypoint correspondences between a source point and a target point cloud can be constructed using the spin image's correspondences. [PERSON] et al. [8] suggested a 3D shape context (3 DSC) descriptor. As the local reference axis, they use the normal at a keypoint. After that, the spherical neighborhood is partitioned evenly along the azimuth and elevation dimensions but logarithmically along the radial dimension. The weighted number of points that fall within each bin is used to construct the 3 DSC descriptor. [PERSON] et al. [9] offered the Point Feature Histogram (PFH) descriptor. A Darboux frame is defined initially for each pair of points in the neighborhood of a keypoint. They then compute four measures based on the angles between the normals of the points and the distance vector between them and add these measures for all pairs of points to form a 16-bin histogram. Point clouds are finally registered and aligned into a consistent global point cloud using corresponding feature point pairs. [PERSON] et al. [10] put forward the SHOT descriptor which encodes the histograms of surface normals at various spatial positions in the reference point neighborhood. [PERSON] et al. [11] presented the RoPS descriptor which employs the rotation and projection mechanism to encode the local surfaces. [PERSON] et al. suggested Fast PointFeature Histograms (FPFHs) [12] which modify PFH descriptors' mathematical expressions and perform a rigorous analysis of their robustness and complexity for the problem of 3D registration for overlapping point cloud views. [PERSON] et al. [13] put forward BRoPH, which is generated directly from the point cloud by turning the description of the 3D point cloud into a series binarization of 2D image patches. [PERSON] et al. [14] proposed KDD, which encodes the information of the whole 3D space around the feature point via kernel density estimation and provides the strategy for selecting different matching metrics for datasets with diverse levels of resolution qualities.  
Fine registration is a more precise and refined registration based on coarse registration, and the essential idea is to immediately acquire the correspondence between a source point cloud and a target point cloud. The ICP (Iterative Closest Point) technique [15; 16] is the most extensively used fine registration method. It is based on the quaternions-based approach and reduce point-to-point distances in overlapping areas between point clouds. However, ICP often needs adequate starting transformation settings, and its iteration process is lengthy. As a result, various ICP variations [17; 18; 19; 20; 21; 22; 23; 24; 25] have been developed to solve these issues.