  Recent self-training techniques have shown notable improvements in
unsupervised domain adaptation for 3D object detection (3D UDA). These
techniques typically select pseudo labels, i.e., 3D boxes, to supervise models
for the target domain. However, this selection process inevitably introduces
unreliable 3D boxes, in which 3D points cannot be definitively assigned as
foreground or background. Previous techniques mitigate this by reweighting
these boxes as pseudo labels, but these boxes can still poison the training
process. To resolve this problem, in this paper, we propose a novel pseudo
label refinery framework. Specifically, in the selection process, to improve
the reliability of pseudo boxes, we propose a complementary augmentation
strategy. This strategy involves either removing all points within an
unreliable box or replacing it with a high-confidence box. Moreover, the point
numbers of instances in high-beam datasets are considerably higher than those
in low-beam datasets, also degrading the quality of pseudo labels during the
training process. We alleviate this issue by generating additional proposals
and aligning RoI features across different domains. Experimental results
demonstrate that our method effectively enhances the quality of pseudo labels
and consistently surpasses the state-of-the-art methods on six autonomous
driving benchmarks. Code will be available at
https://github.com/Zhanwei-Z/PERE.
