## 1 Introduction  
Fast and accurate semantic segmentation of point clouds data of mobile LiDAR systems (MLS) is an essential foundation for smart cities, digital twins, and other fields [1, 2]. Mobile LiDAR systems (MLS) can rapidly collect point clouds data around roads [3, 4, 5]. These point clouds data have a wide range of applications: deformation monitoring [6], architectural heritage preservation [7], urban planning [8], and autonomous driving [9] are the most common areas of their use, and efficient and accurate semantic segmentation is a significant challenge in future research. The point clouds data of the road scene scanned by MLS at high speed is not as dense and uniform as the indoor point clouds data. A large amount of missing and noise in the road point clouds data during the high-speed scanning process because when LiDAR scans the data, it is sparse in the distance and dense in the near. The data collection work under different vehicle speeds causes the inhomogeneous of the point clouds data of different road sections, (as in Figure 1). MLS can quickly acquire the surrounding feature point clouds data, but the amount acquired is huge, and the time required for manual processing is long. Therefore, how to solve the problem of automatic segmentation of road scenes with a large number of inhomogeneous sparse point clouds data has become a current trend for research. At the beginning of the study, researchers tried to apply traditional machine learning methods to solve the problem of road point clouds segmentation, which requires designing manual features, such as geometric features, topological features, etc., and then using support vector machines, conditional random fields, and random forests for point clouds segmentation. However, these manual features require a priori knowledge and are not ideal for point clouds segmentation. With the advancement of technical methods and the incredible popularity of convolutional neural networks (CNNs) for 2D target recognition, more researchers have extended deep learning to 3D [10]. However, CNNs cannot be directly applied to the analysis of point clouds data because the unstructured nature of 3D point clouds is not the same as 2D images. Before the emergence of Pointnet [11], there were roughly three deep learning models applied to point clouds: the voxel model based on 3 DCNN [12], mapping point clouds to 2D space using CNNs for classification [13], and using standard artificial features such as normal vectors, local pump density, and local curvature. After the emergence of Pointnet, which directly implemented the end-to-end learning process of point clouds, the emergence of Pointnet++ [14] improved the sampling method. With the success of Pointnet [11] and Pointnet++ [14], a series of network structures were proposed for solving the problem of automatic point clouds segmentation, such as Pointconv [15], PointSIFT [16], PointCNN [17], etc. Deep learning networks based  
Figure 1: (**a**) Schematic diagram of data collected by mobile LiDAR line scan; (**b**) schematic diagram of the width between scanning lines of point clouds data collected by mobile LiDAR system in low-speed driving; (**c**) schematic diagram of the width between scanning lines of point clouds data collected by mobile LiDAR system in high-speed driving. on Pointnet++ [14] are used to solve the problems of 3D point clouds classification and semantic segmentation of scenes, which have widely influenced the automatic point clouds segmentation field. Although Pointnet++ [14] and other derived network models performed well for various tasks, they are still quite challenging for applications in the semantic segmentation of road scenes with large-scale inhomogeneous sparse point clouds. Inspired by the Pointnet++ [14] network, we propose a new end-to-end approach to exploit road point clouds data that achieves high segmentation accuracy in inhomogeneous sparse MLS road point clouds data.