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.

为了确保自主驾驶平台的安全行驶，除了开发高性能的物体检测技术外，还需要建立一个反映各种城市环境和物体特征的多样化和代表性的数据集。为了解决这两个问题，我们构建了一个多类别的 3D LiDAR 数据集，反映了多样化的城市环境和物体特征，并开发了基于多个教师框架的强大的 3D 半监督目标检测器。我们提出了一种简单而有效的增强技术，基于饼状点补偿（PieAug）的增强，以使教师网络生成高质量的伪标签。对 WOD、KITTI 和我们的数据集进行的大量实验验证了我们提出的方法的有效性和数据集的质量。实验结果表明，我们的方法始终优于现有的最先进的 3D 半监督目标检测方法。我们计划在不久的将来在我们的 Github 存储库上发布我们的多类别 LiDAR 数据集和源代码。