Semi-supervised 3D object detection is a promising yet under-explored direction to reduce data annotation costs, especially for cluttered indoor scenes. A few prior works, such as SESS and 3DIoUMatch, attempt to solve this task by utilizing a teacher model to generate pseudo-labels for unlabeled samples. However, the availability of unlabeled samples in the 3D domain is relatively limited compared to its 2D counterpart due to the greater effort required to collect 3D data. Moreover, the loose consistency regularization in SESS and restricted pseudo-label selection strategy in 3DIoUMatch lead to either low-quality supervision or a limited amount of pseudo labels. To address these issues, we present a novel Dual-Perspective Knowledge Enrichment approach named DPKE for semi-supervised 3D object detection. Our DPKE enriches the knowledge of limited training data, particularly unlabeled data, from two perspectives: data-perspective and feature-perspective. Specifically, from the data-perspective, we propose a class-probabilistic data augmentation method that augments the input data with additional instances based on the varying distribution of class probabilities. Our DPKE achieves feature-perspective knowledge enrichment by designing a geometry-aware feature matching method that regularizes feature-level similarity between object proposals from the student and teacher models. Extensive experiments on the two benchmark datasets demonstrate that our DPKE achieves superior performance over existing state-of-the-art approaches under various label ratio conditions. The source code will be made available to the public.

翻译：弱监督三维目标检测是一个有前景但尚未充分探索的方向，尤其适用于杂乱室内场景下降低数据标注成本。现有工作如SESS和3DIoUMatch尝试通过教师模型为无标签样本生成伪标签来解决该任务。然而，由于三维数据采集所需工作量更大，三维领域中无标签样本的可用性相较于二维领域相对有限。此外，SESS中松散的连续性约束和3DIoUMatch中受限的伪标签选择策略导致监督质量低下或伪标签数量不足。为解决这些问题，我们提出了一种名为DPKE的新型双视角知识增强方法，用于弱监督三维目标检测。我们的DPKE从数据视角和特征视角两个维度丰富有限训练数据（尤其是无标签数据）的知识：在数据视角上，我们提出一种基于类概率分布增强的数据增广方法，根据类别概率的动态分布为输入数据补充额外实例；在特征视角上，我们设计了一种几何感知特征匹配方法，通过正则化学生模型与教师模型生成的目标提议间的特征级相似性实现知识增强。在两个基准数据集上的大量实验表明，在不同标签比例条件下，我们的DPKE均优于现有最先进方法。源代码将公开提供。