Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. While effective, existing DA methods suffer from a substantial drop in performance when applied to a multi-class training setting, due to the co-existence of low-quality pseudo labels and class imbalance issues. In this paper, we address this challenge by proposing a novel ReDB framework tailored for learning to detect all classes at once. Our approach produces Reliable, Diverse, and class-Balanced pseudo 3D boxes to iteratively guide the self-training on a distributionally different target domain. To alleviate disruptions caused by the environmental discrepancy (e.g., beam numbers), the proposed cross-domain examination (CDE) assesses the correctness of pseudo labels by copy-pasting target instances into a source environment and measuring the prediction consistency. To reduce computational overhead and mitigate the object shift (e.g., scales and point densities), we design an overlapped boxes counting (OBC) metric that allows to uniformly downsample pseudo-labeled objects across different geometric characteristics. To confront the issue of inter-class imbalance, we progressively augment the target point clouds with a class-balanced set of pseudo-labeled target instances and source objects, which boosts recognition accuracies on both frequently appearing and rare classes. Experimental results on three benchmark datasets using both voxel-based (i.e., SECOND) and point-based 3D detectors (i.e., PointRCNN) demonstrate that our proposed ReDB approach outperforms existing 3D domain adaptation methods by a large margin, improving 23.15% mAP on the nuScenes $\rightarrow$ KITTI task. The code is available at https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet.

翻译：借助伪标签技术的无监督域自适应已成为域自适应3D目标检测的关键方法。尽管现有方法在单类训练场景下表现有效，但应用于多类训练时，由于低质量伪标签与类别不平衡问题的共存，性能会大幅下降。本文通过提出一种专为同时检测所有类别而设计的ReDB框架来解决这一挑战。该方法通过生成可靠、多样且类别平衡的伪3D边界框，迭代引导在分布差异较大的目标域上进行自训练。为缓解环境差异（如激光束数量）造成的干扰，所提出的跨域检验机制通过将目标实例复制粘贴至源环境并测量预测一致性来评估伪标签的正确性。为降低计算开销并减轻目标偏移（如尺度与点密度差异），我们设计了一种重叠框计数度量方法，能够根据不同的几何特征对伪标签对象进行均匀降采样。针对类别间不平衡问题，我们通过类别平衡的伪标签目标实例与源域对象逐步增强目标点云，从而提升频繁出现类与稀有类的识别精度。在三个基准数据集上使用基于体素（即SECOND）和基于点（即PointRCNN）的3D检测器进行的实验表明，所提出的ReDB方法以较大优势超越现有3D域自适应方法，在nuScenes→KITTI任务中mAP提升23.15%。代码已开源：https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet