Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently. Though preliminary progress has been made, the performance gap between the UDA-based 3D model and the supervised one trained with fully annotated target domain is still large. This motivates us to consider selecting partial-yet-important target data and labeling them at a minimum cost, to achieve a good trade-off between high performance and low annotation cost. To this end, we propose a Bi-domain active learning approach, namely Bi3D, to solve the cross-domain 3D object detection task. The Bi3D first develops a domainness-aware source sampling strategy, which identifies target-domain-like samples from the source domain to avoid the model being interfered by irrelevant source data. Then a diversity-based target sampling strategy is developed, which selects the most informative subset of target domain to improve the model adaptability to the target domain using as little annotation budget as possible. Experiments are conducted on typical cross-domain adaptation scenarios including cross-LiDAR-beam, cross-country, and cross-sensor, where Bi3D achieves a promising target-domain detection accuracy (89.63% on KITTI) compared with UDAbased work (84.29%), even surpassing the detector trained on the full set of the labeled target domain (88.98%). Our code is available at: https://github.com/PJLabADG/3DTrans.

翻译：无监督域自适应（UDA）技术近年来已被探索用于三维跨域任务。尽管已取得初步进展，但基于UDA的三维模型与使用完整标注目标域数据训练的有监督模型之间的性能差距仍然较大。这促使我们考虑选取部分但关键的目标域数据并以最低成本进行标注，从而在高性能与低标注成本之间取得良好平衡。为此，我们提出一种双域主动学习方法——Bi3D，以解决跨域三维目标检测任务。Bi3D首先开发了一种域感知源域采样策略，从源域中识别出类似目标域的样本，避免模型受到无关源域数据的干扰。进而提出基于多样性的目标域采样策略，在尽可能少的标注预算内，选取目标域中最具信息量的子集，以提升模型对目标域的适应性。在典型跨域自适应场景（包括跨激光雷达线束、跨国家和跨传感器）上的实验表明，Bi3D取得了令人满意的目标域检测精度（KITTI数据集上达89.63%），优于基于UDA的方法（84.29%），甚至超越了使用完整标注目标域数据训练的检测器（88.98%）。我们的代码已开源至：https://github.com/PJLabADG/3DTrans。