## 1 Introduction  
With the rapid development of mobile robot technology and artificial intelligence, ground unmanned vehicles have been widely used in various fields such as logistics, transportation, security, inspection, and so on [1]. Driverless technology can improve production efficiency, reduce safety risks, reduce operating costs, increase resource utilization, and promote industrial transformation and upgrading [2]. Meanwhile, the traditional automotive industry, relying on the development of artificial intelligence, is vigorously researching and developing autonomous driving technology. The outstanding role of autonomous driving technology in reducing driver intensity and improving driving safety has given this technology a promising development prospect. The core technical system of autonomous driving can be mainly divided into three levels: perception, decision-making, and execution. Perceiving and locating the surrounding environment is a prerequisite for autonomous driving technology. Currently, there are roughly two approaches to autonomous driving perception technology: one is a machine vision-centric solution with millimeter-wave radar and cameras, typically represented by companies such as Tesla, Mobileye, Baidu Apollo, etc. ; the other is a sensor route centered on high-precision maps and LiDAR, represented by companies such as Waymo and Huawei. In the debate between pure vision and LiDAR, some argue that pure vision solutions are sufficient for most road scenarios, while LiDAR is merely a complement. They believe that human drivers primarily rely on visual information during driving, making pure vision solutions more aligned with human driving habits. Furthermore, with the continuous development of computer vision technology, the accuracy and reliability of pure vision solutions are constantly improving. However, others contend that LiDAR is crucial for autonomous vehicles. They argue that in complex road scenarios and adverse weather conditions, pure vision solutions struggle to ensure accuracy, whereas LiDAR can provide more reliable information. Additionally, LiDAR can be fused with other sensors to further enhance the accuracy and safety of autonomous vehicles. Previously, pure vision solutions were held as overwhelmingly dominant. However, with the continuous advancement of LiDAR technology, an increasing number of vehicles are being equipped with LiDAR. Furthermore, the declining cost of LiDAR will further promote its development. Therefore, processing the data collected by LiDAR has become increasingly important. The 3D point clouds obtained from LIDAR scanning are represented as a set of points. The significance of semantic segmentation lies in assigning labels to each point, allowing for differentiation of the entire point clouds and achieving the purpose of initial visual understanding. With semantically calibrated 3D point clouds, unmanned vehicles can accurately identify actionable areas, enabling better decision-making and control. It can also extract slope surfaces for safe slope calculations to prevent accidents. Therefore, accurate semantic segmentation of 3D point clouds is a crucial prerequisite for constructing driverless technology, and its precision determines the reliability of autonomous driving, the accuracy of safety inspections, and the precision of 3D modeling. In recent years, many deep learning methods have been applied to 3D point cloud semantic segmentation [3; 4]. These methods can be roughly classified into three categories: projection-based methods, voxel-based methods, and direct point-based methods. Projection-based methods project the 3D point clouds onto a plane and turn them into 2D images, then use 2D images for semantic segmentation, which lose the spatial characteristics of the original 3D point clouds. Voxel-based methods transform 3D point clouds into dense or sparse 3D grids. Semantic segmentation is performed using standard 3D convolution.