Point cloud target segmentation is the key to perceive targets for a smart car using three-dimensional (3D) LiDAR. Aiming at the problems of poor real-time and low accuracy of the existing in 3D LiDAR point cloud target segmentation algorithms, an approach based on a depth map is proposed in this paper to realize fast and accurate segmentation for point cloud target segmentation. The original data are transformed into a depth map, and the mapping relationship between point cloud data and a depth map is established. After removing the ground point cloud data by using the angle threshold of the LiDAR scanning line, the non-ground point cloud is clustered and segmented by the improved DBSCAN(Density-Based Spatial Clustering of Applications with Noise) algorithm combined with the depth map and the adaptive parameters. Experimental results show that the proposed method has a significant improvement in time efficiency compared with the traditional clustering algorithms. Moreover, the under-segment error rate is decreased while the segmentation accuracy is increased by 10% to 85.02%.