# Semi-Supervised Semantic Segmentation Network for Point Clouds Based on 3D Shape
## 1 Introduction  
The 21 st century is witnessing rapid development in the field of data science, with artificial intelligence playing a major role in driving innovation across various industries. One of the areas in which AI has made significant advancements is computer vision, which has been widely used in smart driving, smart cities, remote sensing, and robotics. In recent years, with the development of LiDAR technology, research has shifted from two-dimensional (2D) to three-dimensional (3D) vision, which has important applications in areas such as autonomous driving, industrial control, remote sensing, and mapping. The main forms of 3D data representation include point clouds, voxels, multi-views, and meshes. Among them [1], 3D point clouds, which reflect the surface and structural information of 3D objects better than voxels, multi-views, and meshes, are becoming increasingly popular in the industry. However, the irregular and disorderly nature of point cloud data presents challenges to the application of deep learning methods, which are commonly used for 2D images. The representation of 3D shape in point cloud data is described by point position, color, normal vector, and other information. By analyzing the 3D shape in the point cloud data, we can understand the geometric shape, surface texture, and surface shape of the object. At the same time, geometric shapes, which reflect the intrinsic connections between local structures in point clouds, play a crucial role in intelligent reasoning and provide important information about the object's local structures and semantics. Therefore, extracting robust shape features from point clouds is a crucial step in the analysis of point cloud data. Despite the advances made in this field, this challenge has yet to be addressed. To address the challenges of processing 3D point cloud data, [PERSON] et al. [2] proposed the PointNet network, which directly processed a point cloud using deep learning andachieved good results. However, this network considered only the global features of the point cloud, ignoring local features. Subsequently, [PERSON] et al. [3] developed PointNet++, which added local information but still ignored local shape information between points. [PERSON] et al. [4] attempted to solve this issue using convolutional neural networks (CNN) to learn 3D shapes and generate local descriptors. However, the results were not sufficiently comprehensive because they relied on projections to obtain local surface information. To overcome these limitations, we propose a semi-supervised semantic segmentation network for point clouds based on 3D shape, which we call SBSNet. The network encodes the shape of the extracted point cloud from multiple pieces of geometric information and constructs the shape context information through an attention mechanism to improve the 3D point cloud data feature calculation, capture the significant features of the data, and enhance the feature difference degree of the boundary points. The key contributions of our work are as follows:  
(1) The proposed network extracts multiple pieces of geometric information from point cloud data, such as density, normal vectors, and other geometric features that can reflect point cloud texture information, uses unsupervised methods to zone the point cloud's geometric shape information, and finally encodes the shape information using neural networks. (2) We introduce an improved furthest point sampling algorithm based on geometric curvature combined with spherical queries to create multiple subsets of local point cloud data, partition the data into regions, and build a high-dimensional feature extractor for the local data subsets. (3) The attention mechanism is designed to capture the contextual information of the point cloud shape features, amplify the significance of the extracted local features, and finally improve the accuracy of point cloud segmentation. (4) SBSNet was experimentally compared with several networks, such as PointNet, PointNet++, and 3P-RNN.