Recent self-training techniques have shown significant improvements in unsupervised domain adaptation for 3D object detection (3D UDA).These techniques typically select pseudo labels (i.e. 3D boxes) to supervise the target domain. However, this selection process inevitably introduces unreliable 3D boxes, as the 3D points inside them cannot be definitively assigned as foreground or background points.Previous techniques reweight these unreliable boxes to reduce unreliability, but these boxes could still poison the training process.To resolve this problem, in this paper, we propose a pseudo label refinery framework.Specifically, in the selection process, to mitigate the unreliability of pseudo boxes, we propose a complementary augmentation strategy.This strategy either removes all points within each unreliable box or alternatively replaces it with a high-confidence box.Furthermore, 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.