# A LiDAR Multi-Object Detection Algorithm for Autonomous Driving
## 1 Introduction  
Three-dimensional object detection is the core of an autonomous driving perception system, which detects and analyzes targets around the vehicle to obtain their size, shape, category, and motion characteristics, providing reliable operational decisions for realizing autonomous driving [1; 2; 3]. In recent years, scholars at home and abroad have proposed a variety of effective and feasible 3D object detection algorithms according to the categories of sensors used in the visual perception system, among which the method based on LIDAR point cloud has been deeply studied due to its excellent detection performance [4]. Depending on how the point cloud is processed, it is further categorized into multi-view based, point based, and voxel based methods [5]. Multi-view based methods first convert the point cloud into a front view or bird's eye view (BEV), and then use mature 2D object detection algorithms for feature extraction [6]. For example, VeloFCN [7] first converts the point cloud into a front view 2D feature map and then uses a 2D object detection algorithm for object detection, which combined with VoxelNet [8] and Fully Convolutional Networks (FCNs) performs well in multi-category and multi-object detection tasks, and can detect pedestrians, vehicles, bicycles, and other multiple objects simultaneously. However,the algorithm consumes a large amount of memory and computational resources, resulting in slower network training and inference, which requires high performance computing devices and longer training and testing time. PIXOR [9] converts the point cloud height information to color channels to achieve a bird's eye view representation, and then uses a 2D detection network for detection, which is applicable to various roadside light environments for cars, solves the occlusion problem of the front view, and has high accuracy and robustness. However, the algorithm has high computational complexity and large data volume, and requires the use of GPU for accelerated computation. Point-based methods directly use sparse point clouds for processing, which enhances the feature extraction and feature characterization of point clouds. For example, PointNet [10] proposed by [PERSON] et al. from Stanford University directly uses the original point cloud to learn spatial information without conversion to other forms, which can be applied to various point cloud processing tasks. Aiming at the problem of PointNet's insensitivity to local features and poor extraction effect, the PointNet++ [11] algorithm optimizes the processing of global features in the PointNet algorithm, and improves the algorithm's ability to perceive both local and global structures through hierarchical aggregation and local feature extraction, and also has good stability for data with noise and sampling density changes. However, the algorithm has difficulty capturing the local feature information of non-uniformly distributed or sparse point clouds in some complex and diverse 3D scene tasks. F-PointNet [12] introduces fine-grained feature representations and multi-scale contextual information, further improves the PointNet algorithm, and can effectively classify, segment, and estimate the point cloud by adding a branching network and extracting the fine-grained feature representations of each point in the point cloud, with better performance [13][PERSON] However, for point clouds with different scales and densities, the performance of this algorithmic model may be degraded, and further optimization and improvement is needed. The output of the model may vary greatly with different arrangements of the input point cloud, and the interpretability is poor[PERSON]  
Voxel based methods usually convert irregular point clouds into regular voxel representations [14]. For example, [PERSON] et al.