# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 1 Introduction  
With the development of digital road mapping technology, 3D scanning technology based on light detection and ranging (LiDAR) has shown promising prospects in the management of road infrastructures [1; 2]. As crucial components of the road traffic system [3], road intersections offer significant value in extracting information. Urban road intersections encompass a variety of ancillary facilities, including traffic lights, drainage, lighting, and greening facilities [4], creating challenges for data collection using LiDAR. Lightweight LiDAR, characterized by its ease of operation, cost-effectiveness, and strong environmental adaptability, employs non-repetitive scanning methods and fewer sensors [5], thereby simplifying the complex and time-consuming multi-beam laser calibration process. Compared to vehicle-mounted LiDAR and airborne LiDAR, lightweight LiDAR typically uses a static installation that is unaffected by the movement of the vehicle or aircraft, resulting in more stable data acquisition. In addition, given the complexity of the facilities, vehicles, and pedestrians at road intersections, lightweight LiDAR can avoid irrelevant factors affecting data quality by acquiring multi-angle point cloud data. The complexity of road intersections places high demands on the performance of semantic segmentation methods. Traditional point cloud semantic segmentation methods often employ supervised learning algorithms that use, for example, attribute clustering [6; 7; 8], model fitting [9], and region growing [10; 11; 12; 13]. However, the segmentationaccuracy of these algorithms is usually limited by manually set experience thresholds, making them unsuitable for the complex and variable scenarios of road intersections. In recent years, many scholars have achieved promising results in semantic segmentation using deep learning methods [14] on multiple public point cloud datasets [15; 16]. Artificial neural networks with multiple overlapping hidden layers possess strong feature learning capabilities [17]. Artificial neural networks project point clouds into a grid structure, converting raw data into 2D images [18; 19] or 3D voxels for processing [20; 21], with segmentation outcomes heavily dependent on the data quality of point clouds from different perspectives (e.g., mutual occlusion). This method also struggles to balance feature completeness with computational resource consumption [22], making it unsuitable for the semantic segmentation of road scene point clouds. Point-based semantic segmentation methods, on the other hand, directly use point clouds as network inputs for end-to-end spatial feature learning [23] with strong robustness and generalization capabilities. For processing large-scale, multi-category point cloud scenes, networks such as RandLA-Net [24], BAAF-Net [25], and SCF-Net [26] often employ random sampling methods with low computational complexity and high storage efficiency. Despite the introduction of structures to capture local features, the sparse distribution and uneven granularity of downsampled point clouds still affect model training effectiveness. The BAF-LAC network [27], building on the RandLA-Net framework, introduced a backward attention fusion mechanism and a local aggregation classifier, thereby enhancing point cloud classification accuracy. However, large-scale point cloud semantic segmentation models continue to face challenges in fully expressing features, particularly in terms of segmentation performance in road intersection scenarios. The method proposed in this study uses lightweight LiDAR for point cloud data collection at road intersections. After preprocessing steps such as registration stitching and data simplification, we implement a semantic segmentation method for point clouds based on an improved RandLA-Net.