• Corpus ID: 237091793

SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation

  title={SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation},
  author={Qiangeng Xu and Yin Zhou and Weiyue Wang and C. Qi and Drago Anguelov},
In autonomous driving, a LiDAR-based object detector should perform reliably at different geographic locations and under various weather conditions. While recent 3D detection research focuses on improving performance within a single domain, our study reveals that the performance of modern detectors can drop drastically cross-domain. In this paper, we investigate unsupervised domain adaptation (UDA) for LiDAR-based 3D object detection. On the Waymo Domain Adaptation dataset, we identify the… 
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    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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A domain adaptation like approach is innovatively proposed to enhance the robustness of the feature representation in 3D object detection and mimics the functionality of the human brain when proceeding object perception.
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