3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive Clustering and 3D Point Spatial Histograms

The advent of 3D LiDAR technology has revolutionized object detection in applications such as autonomous driving, robotics, and advanced driver assistance systems. However, existing methods often require substantial computational resources, limiting their practicality for real-time applications on devices with constrained hardware capabilities. This paper presents an efficient and lightweight 3D LiDAR object detection framework, 3D-PSH, that combines adaptive clustering with 3D Point Spatial Histograms (3D-PSH) and classical classification techniques to address these challenges. Our framework begins with an adaptive clustering algorithm that segments the point cloud data into distinct clusters, representing potential objects. 3D Point Spatial Histograms (3D-PSH) are then computed from these clusters and subsequently quantized into a Bag of Visual Words (BoVW) to create a compact and informative representation. These representations are then classified using robust classical classification methods to identify object types, such as pedestrians and vehicles. This multi-step approach ensures a balance between computational efficiency and detection accuracy, making it suitable for real-time deployment. Extensive experiments on the KITTI dataset and our live sensor data demonstrate the effectiveness and efficiency of our proposed framework. The results indicate that our method achieves competitive accuracy while significantly reducing computational requirements compared to traditional approaches. This framework offers a practical solution for deploying 3D object detection in a wide range of applications, particularly where computational resources are limited.