For safe urban driving in autonomous driving platforms, it is critical not only to develop high-performance object detection methods but also to build a training dataset that accurately reflects the diverse environments and object characteristics present in urban settings. To tackle these challenges, in previous study, we created a multi-class 3D LiDAR dataset that incorporated various urban environments and object types. In this paper, we have developed an efficient 3D semi-supervised object detector based on a dual-teacher framework. In this framework, similar classes are grouped into categories, with each category assigned a teacher. By leveraging these teachers, the student model gradually improves, resulting in an efficient object detector. The experiments on the WOD and KITTI validate the effectiveness of our proposed method, and the results demonstrate that our approach consistently outperforms existing state-of-the-art 3D semi-supervised object detection methods.
