# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 2 Related Works
### Point-Cloud-Based Methods  
The above two methods have high computational complexity. In view of this, the network model based on the original point cloud is gradually proposed. PointNet [22] was among the earliest approaches to apply deep learning network architectures for processing raw point cloud data. A limitation of this method is that it only extracts global features of point clouds to enable point cloud classification or semantic segmentation without accounting for local features, leading to less-than-ideal network performance for semantic segmentation in large scenes. In order to improve the segmentation performance of point clouds, [PERSON] et al. [23] proposed PointNet++ by improving the PointNet framework. PointNet++ aims to address the limitation of the original PointNet that only considers global features of point clouds. The addition of the local region segmentation module allows PointNet++ to learn and utilize local geometric features at different scales, thereby improving its performance on large-scene semantic segmentation. [PERSON] et al. [24] proposed a hierarchical point-edge interaction network for point cloud semantic segmentation. It introduces an edge convolution operation to interact between point features and edge features for capturing local geometric structures. Unbalanced point cloud scenarios will affect the semantic segmentation accuracy of PointNet++. In view of this problem, [PERSON] et al. [25] proposed a weighted sampling method based on farthest point sampling (FPS), which can make the sampling process more balanced and reduce the influence of point cloud scene imbalance. [PERSON] et al. [26] proposed hierarchical parallel group convolution, which can capture both a single point feature and local geometric features of point clouds. Therefore, the model improves the ability of the network to recognize complex classes. [PERSON] et al. [27] proposed a multi-feature PointNet (MSMF-PointNet) deep learning point cloud model that can extract multiscale, multi-neighborhood features for classification. [PERSON] et al. [28] introduces a relation module to learn the relations between points and aggregate neighbor information. Additionally, a shape module is presented to encode absolute geometric features of each point. The two modules are combined to extract comprehensive shape representations. The network can reason about the spatial information features of the point cloud and realize the shape context perception. The global features are obtained through the fully connected layer and then classified. [PERSON] et al. [29] proposed an efficient and lightweight RandLA-Net network. The model used a local feature aggregation module to expand the range of k-nearest neighbor points to reduce information loss. The random sampling is used to reduce the storage cost and improve computational efficiency. [PERSON] et al. [30] proposed a method for large-scale point cloud semantic segmentation using superpoint graphs. The approach first oversegments the point cloud into a set of superpoints. It then constructs a graph to represent relationships between superpoints. Graph convolutional networks are applied to perform semantic segmentation on the superpoint graph. It can greatly reduce the number of points in a point cloud, and the network can be applied in the field of large-scale point cloud datasets.