RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation

@article{Cardace2021RefRecPR,
  title={RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation},
  author={Adriano Cardace and Riccardo Spezialetti and Pierluigi Zama Ramirez and Samuele Salti and Luigi di Stefano},
  journal={2021 International Conference on 3D Vision (3DV)},
  year={2021},
  pages={331-341}
}
Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. Reliance on multi-task learning to align features across domains has been the standard way to tackle it. In this paper, we take a different path and propose RefRec, the first approach to investigate pseudo-labels and self-training in UDA for point clouds. We present two main innovations to make self-training effective on 3D data: i) refinement of noisy pseudo… 

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