Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection

Zhanwei Zhang, Minghao Chen, Shuai Xiao, Liang Peng, Hengjia Li, Binbin Lin, Ping Li, Wenxiao Wang, Boxi Wu, Deng Cai; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15291-15300

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.