To ensure safe urban driving for autonomous platforms, it is crucial not only to develop high-performance object detection techniques but also to establish a diverse and representative dataset that captures various urban environments and object characteristics. To address these two issues, we have constructed a multi-class 3D LiDAR dataset reflecting diverse urban environments and object characteristics, and developed a robust 3D semi-supervised object detection (SSOD) based on a multiple teachers framework. This SSOD framework categorizes similar classes and assigns specialized teachers to each category. Through collaborative supervision among these category-specialized teachers, the student network becomes increasingly proficient, leading to a highly effective object detector. We propose a simple yet effective augmentation technique, Pie-based Point Compensating Augmentation (PieAug), to enable the teacher network to generate high-quality pseudo-labels. Extensive experiments on the WOD, KITTI, and our datasets validate the effectiveness of our proposed method and the quality of our dataset. Experimental results demonstrate that our approach consistently outperforms existing state-of-the-art 3D semi-supervised object detection methods across all datasets. We plan to release our multi-class LiDAR dataset and the source code available on our Github repository in the near future.

翻译：为确保自动驾驶平台的城市行驶安全，不仅需要开发高性能目标检测技术，更需构建能够涵盖多样化城市场景与目标特征的具有代表性的数据集。针对这两个问题，我们构建了反映多元城市场景与目标特征的多类别三维激光雷达数据集，并基于多教师框架开发了鲁棒的三维半监督目标检测方法。该半监督检测框架通过对相似类别进行分组并为每个类别分配专用教师网络，借助这些类别专用教师网络的协同监督机制，使学生网络的能力持续提升，最终形成高效的目标检测器。我们提出了一种简洁而有效的增强技术——基于扇区的点云补偿增强方法，使教师网络能够生成高质量的伪标签。在WOD、KITTI及我们自建数据集上的大量实验验证了所提方法的有效性与数据集的质量。实验结果表明，我们的方法在所有数据集上均持续优于现有的三维半监督目标检测先进方法。我们计划在近期于GitHub仓库开源多类别激光雷达数据集及相关源代码。