MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency, ICCV 2023
Qiao Wu, Jiaqi Yang, Kun Sun, Chu'ai Zhang, Yanning Zhang, Mathieu Salzmann

We propose the first semi-supervised approach to 3D Single Object Tracking. Our method, MixCycle, uses self and forward-backward cycle-consistency for supervision, and introduce a data augmentation strategy named SOTMixup to improve the tracker's robustness to point cloud diversity. It is generalizes to appearance matching-based trackers.

@InProceedings{Wu_2023_MixCycle,
  author    = {Wu, Qiao and Yang, Jiaqi and Sun, Kun and Zhang, Chu'ai and Zhang, Yanning and Salzmann, Mathieu},
  title     = {MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2023},
  pages     = {13956-13966}
}