# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 1 Introduction  
Currently, laser point clouds have been widely used in many fields, such as autonomous driving [1], augmented reality [2] and so on. Point cloud classification and segmentation are key to scene understanding. Therefore, many scholars have systematically studied point cloud classification and segmentation. Machine learning approaches [3] are the most commonly used data analysis methods. In the field of point cloud data analysis, deep learning is used for point cloud classification and segmentation. To improve the accuracy of point cloud classification and segmentation, some researchers have drawn on image methods to handle point clouds. They project point clouds onto a 2D plane to transform them into images so that point cloud features can be learned based on 2D image feature learning methods [4]. The advantage of these methods is that the algorithms are simple. The limitations of these methods are that the process of converting point clouds to images can easily lose some feature information, and the selection of projection views requires a lot of prior knowledge. To address this, some researchers have begun to implement point cloud segmentation and classification directly on point clouds, such as voxel-based methods [5; 6; 7]. This method expresses the entire point cloud in the form of individual voxels, thereby retaining the geometric information of objects. The disadvantage of these methods is that they cannot finely divide the geometric information of object boundaries. In addition, these methods are generally subject to severe memorylimitations, with high resolution incurring enormous computational and storage costs, while low resolution would result in information loss. In view of this, the sparse convolution [8] was used to reduce memory usage. For the low-resolution point clouds, some researchers have used point-based methods [9; 10] to implement point cloud segmentation, which can obtain local geometric information of low-resolution point clouds. However, relying solely on local geometric information to determine the overall structure may lack consideration of global structural topological information. Simultaneously, these methods require a significant amount of time. The methods that directly process point cloud data can fully learn all features of point clouds without requiring additional point cloud transformation operations. However, the raw point clouds have the characteristics of being irregular, sparse and unordered. Therefore, it is necessary to construct the raw point clouds into a structure of local neighborhood graphs.  
To address the above issues, this paper proposes a point cloud deep learning network based on multi-level feature fusion in a local domain. First, the local neighborhoods of the point cloud are constructed using graph convolution to extract features. Then, the feature points are used as input again to reconstruct local neighborhoods. In each neighborhood, a central point is found, and the dependencies between the central point and other feature points within the neighborhood are learned. Finally, point cloud classification and segmentation are achieved through pooling processing based on different features.