In the field of domain adaptation (DA) on 3D object detection, most of the work is dedicated to unsupervised domain adaptation (UDA). Yet, without any target annotations, the performance gap between the UDA approaches and the fully-supervised approach is still noticeable, which is impractical for real-world applications. On the other hand, weakly-supervised domain adaptation (WDA) is an underexplored yet practical task that only requires few labeling effort on the target domain. To improve the DA performance in a cost-effective way, we propose a general weak labels guided self-training framework, WLST, designed for WDA on 3D object detection. By incorporating autolabeler, which can generate 3D pseudo labels from 2D bounding boxes, into the existing self-training pipeline, our method is able to generate more robust and consistent pseudo labels that would benefit the training process on the target domain. Extensive experiments demonstrate the effectiveness, robustness, and detector-agnosticism of our WLST framework. Notably, it outperforms previous state-of-the-art methods on all evaluation tasks.

翻译：在3D目标检测的域适应领域，现有工作主要集中于无监督域适应。然而，由于缺乏目标域标注，无监督域适应方法与全监督方法之间的性能差距仍较为显著，这在实际应用中难以满足需求。另一方面，弱监督域适应是一项探索不足但具有实用价值的任务，仅需对目标域进行少量标注工作。为以经济有效的方式提升域适应性能，我们提出了一种通用的弱标签引导自训练框架WLST，专门用于3D目标检测的弱监督域适应。通过将自动标注器（可从2D边界框生成3D伪标签）融入现有自训练流程，该方法能够生成更鲁棒且一致的伪标签，从而促进目标域的训练过程。大量实验证明了WLST框架的有效性、鲁棒性及检测器无关性。值得注意的是，该方法在所有评估任务上均超越了先前的最优方法。