3D object detection from point clouds is crucial in safety-critical autonomous driving. Although many works have made great efforts and achieved significant progress on this task, most of them suffer from expensive annotation cost and poor transferability to unknown data due to the domain gap. Recently, few works attempt to tackle the domain gap in objects, but still fail to adapt to the gap of varying beam-densities between two domains, which is critical to mitigate the characteristic differences of the LiDAR collectors. To this end, we make the attempt to propose a density-insensitive domain adaption framework to address the density-induced domain gap. In particular, we first introduce Random Beam Re-Sampling (RBRS) to enhance the robustness of 3D detectors trained on the source domain to the varying beam-density. Then, we take this pre-trained detector as the backbone model, and feed the unlabeled target domain data into our newly designed task-specific teacher-student framework for predicting its high-quality pseudo labels. To further adapt the property of density-insensitivity into the target domain, we feed the teacher and student branches with the same sample of different densities, and propose an Object Graph Alignment (OGA) module to construct two object-graphs between the two branches for enforcing the consistency in both the attribute and relation of cross-density objects. Experimental results on three widely adopted 3D object detection datasets demonstrate that our proposed domain adaption method outperforms the state-of-the-art methods, especially over varying-density data. Code is available at https://github.com/WoodwindHu/DTS}{https://github.com/WoodwindHu/DTS.

翻译：从点云进行3D目标检测在安全关键的自动驾驶中至关重要。尽管已有许多工作在此任务上付出了巨大努力并取得了显著进展，但由于域差异，大多数方法仍面临昂贵的标注成本和对未知数据迁移性差的问题。最近，少数工作尝试解决目标域中的差异问题，但未能适应两个域之间变化的波束密度差异，而这对缓解激光雷达采集器的特性差异至关重要。为此，我们尝试提出一种密度不敏感的域适应框架来解决密度引发的域差异。具体而言，我们首先引入随机波束重采样（RBRS）来增强在源域上训练的3D检测器对变化波束密度的鲁棒性。然后，我们将此预训练检测器作为骨干模型，并将未标注的目标域数据输入到我们新设计的任务特定教师-学生框架中，用于预测其高质量伪标签。为了进一步将密度不敏感特性适应到目标域，我们向教师和学生分支馈送不同密度的相同样本，并提出一个对象图对齐（OGA）模块，在两个分支之间构建两个对象图，以强制跨密度对象的属性和关系的一致性。在三个广泛采用的3D目标检测数据集上的实验结果表明，我们提出的域适应方法优于最先进的方法，尤其是在变密度数据上。代码可在 https://github.com/WoodwindHu/DTS 获取。