Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection. Although Self-Training (ST) based cross-domain 3D detection methods with the assistance of pseudo-labeling techniques have achieved remarkable progress, they still face the issue of low-quality pseudo-labels when there are significant domain disparities due to the absence of a process for feature distribution alignment. While Adversarial Learning (AL) based methods can effectively align the feature distributions of the source and target domains, the inability to obtain labels in the target domain forces the adoption of asymmetric optimization losses, resulting in a challenging issue of source domain bias. To overcome these limitations, we propose a novel unsupervised domain adaptation framework for 3D object detection via collaborating ST and AL, dubbed as STAL3D, unleashing the complementary advantages of pseudo labels and feature distribution alignment. Additionally, a Background Suppression Adversarial Learning (BS-AL) module and a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain scenes, effectively alleviating the issues of the large proportion of background interference and source domain size bias. Our STAL3D achieves state-of-the-art performance on multiple cross-domain tasks and even surpasses the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$ KITTI-rain.

翻译：现有3D目标检测方法因标注成本高昂以及域差异导致的未知数据泛化能力不足而面临挑战。无监督域自适应旨在将标注源域训练的检测模型泛化至未探索目标域，为跨域3D目标检测提供了可行解决方案。尽管基于自训练并借助伪标签技术的跨域3D检测方法已取得显著进展，但由于缺乏特征分布对齐过程，在域差异显著时仍面临伪标签质量低下的问题。而基于对抗学习的方法虽能有效对齐源域与目标域的特征分布，却因无法获取目标域标签被迫采用非对称优化损失，导致源域偏差这一难题。为克服这些局限，我们提出一种通过自训练与对抗学习协同实现3D目标检测的新型无监督域自适应框架STAL3D，充分发挥伪标签与特征分布对齐的互补优势。此外，针对3D跨域场景专门设计了背景抑制对抗学习模块与尺度过滤模块，有效缓解背景干扰占比过大及源域尺寸偏差问题。我们的STAL3D在多项跨域任务中达到最先进性能，并在Waymo $\rightarrow$ KITTI与Waymo $\rightarrow$ KITTI-rain任务上甚至超越Oracle基准结果。