## 4 Registration Algorithms Based on Feature
### Feature Extraction
PointNet [110] is the first network to extract features directly from the input point cloud. PointNet primarily addresses the issues of point cloud disorder, displacement invariance, and rotation invariance. The generality of features is, however, constrained by PointNet's inability to capture the semantic information of spatial points. As a solution to the aforementioned issues, PointNet++ [111] adds a hierarchical structure made up of the farthest point sampling layer, a grouping layer, and a PointNet layer to capture information at different scales. PointNet and PointNet++ cannot be directly applied to the field of point cloud registration because they do not emphasize the geometric relationship between points, which restricts the representational potential of features. It does, however, provide an excellent reference for tackling the difficulties of disorder, density change, displacement invariance, and rotation invariance in point cloud feature extraction, as well as facilitating subsequent study. [PERSON] et al. introduced PPFNet [112], which calculates point pair features (PPF) first for the local area around sampling sites [104,105] and merges local and global features of various scales; the final feature is obtained through MLPs coding. PPFNet makes use of global context awareness, encodes features, and increases their resistance to noise. However, the calculation of PPF features necessitates a significant quantity of nearest-neighbor labeled data, and the creation of a local reference frame depends on the estimate of normal vectors, making it sensitive to noise. To improve the disadvantages of PPFNet, [PERSON] et al. also suggested PPF-FoldNet [113]. Before using a framework containing a PointNet encoder, this method will first extract PPF features, and a FoldingNet [136] decoder to produce the final point cloud's feature representation. PPF-FoldNet performs better in noisy environments, although it is still susceptible to changes in point density. [PERSON] and [PERSON] [114] introduced 3 DFeat-Net, which takes into account learning a 3D form feature detector and feature extractor at the same time and exploits weakly supervised training to tackle the relevant point cloud issue. [PERSON] et al. [115] suggested 3 DSmoothNet, which produced a smoothed density value voxelization (SDV) suitable for convolution operations to encode the original point cloud and learn the ultimate features through the Siamese network architecture. The method enhances the generalization ability. The technique can still gain better results if it is trained on a single scene before being applied to multiple scenarios. [PERSON] et al. [116] proposed fully convolutional geometric features (FCCF), which employ sparse convolution instead of traditional convolution to alleviate the problem of point cloud sparsity. The network architecture is constructed by sparse convolution to extract point cloud's local geometric properties. FCGF, on the other hand, has a poor generalization impact due to the overfitting of the data sample. After converting the reflection intensity into 2D images, [PERSON] et al. [117] applied the scale-invariant feature transform (SIFT) to extract feature. It is essential to select the spatial point that corresponds to the extreme value position of the Gaussian DoG as the feature point. PRNet (partial-to-partial registration network) [45] defines the saliency of points by the two-parametric distance of features, and then selects several key points with high significance according to the size of the two-parametric distance. [PERSON] and [PERSON] [118] proposed RPMNet based on robust point matching (RPM) [52]. The feature extraction phase has a PPFNet-like structure, and hybrid features are created by combining features with 3-dimensional point coordinates. The outlier parameters and annealing parameters are estimated by a parameter prediction network, and the point pair matching is then calculated by merging the mixed features.