Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training

@article{Li2022UnsupervisedDA,
  title={Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training},
  author={Zhenyu Li and Zehui Chen and Ang Li and Liangji Fang and Qinhong Jiang and Xianming Liu and Junjun Jiang},
  journal={ArXiv},
  year={2022},
  volume={abs/2204.11590}
}
. Monocular 3D object detection (Mono3D) has achieved un-precedented success with the advent of deep learning techniques and emerging large-scale autonomous driving datasets. However, drastic performance degradation remains an unwell-studied challenge for practical cross-domain deployment as the lack of labels on the target domain. In this paper, we first comprehensively investigate the significant underly-ing factor of the domain gap in Mono3D, where the critical observation is a depth-shift… 
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Towards Model Generalization for Monocular 3D Object Detection
TLDR
The 2D-3D geometry-consistent object scaling strategy (GCOS) is proposed to bridge the gap via an instance-level augment and achieves remarkable performance on all evaluated datasets and surpasses the SoTA unsupervised domain adaptation scheme even without utilizing data on the target domain.

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