  State-of-the-art 3D object detectors are usually trained on large-scale
datasets with high-quality 3D annotations. However, such 3D annotations are
often expensive and time-consuming, which may not be practical for real
applications. A natural remedy is to adopt semi-supervised learning (SSL) by
leveraging a limited amount of labeled samples and abundant unlabeled samples.
Current pseudolabeling-based SSL object detection methods mainly adopt a
teacher-student framework, with a single fixed threshold strategy to generate
supervision signals, which inevitably brings confused supervision when guiding
the student network training. Besides, the data augmentation of the point cloud
in the typical teacher-student framework is too weak, and only contains basic
down sampling and flip-and-shift (i.e., rotate and scaling), which hinders the
effective learning of feature information. Hence, we address these issues by
introducing a novel approach of Hierarchical Supervision and Shuffle Data
Augmentation (HSSDA), which is a simple yet effective teacher-student
framework. The teacher network generates more reasonable supervision for the
student network by designing a dynamic dual-threshold strategy. Besides, the
shuffle data augmentation strategy is designed to strengthen the feature
representation ability of the student network. Extensive experiments show that
HSSDA consistently outperforms the recent state-of-the-art methods on different
datasets. The code will be released at https://github.com/azhuantou/HSSDA.
