Power of Cooperative Supervision: Multiple Teachers Framework for Enhanced 3D Semi-Supervised Object Detection
CoRR(2024)
Abstract
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.
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