PointCMP: Contrastive Mask Prediction for Self-supervised Learning on
  Point Cloud Videos
Zhiqiang Shen Xiaoxiao Sheng Longguang Wang Yulan Guo Qiong Liu Xi Zhou
Self-supervised learning can extract representations of good quality from
solely unlabeled data, which is appealing for point cloud videos due to their
high labelling cost. In this paper, we propose a contrastive mask prediction
(PointCMP) framework for self-supervised learning on point cloud videos.
Specifically, our PointCMP employs a two-branch structure to achieve
simultaneous learning of both local and global spatio-temporal information. On
top of this two-branch structure, a mutual similarity based augmentation module
is developed to synthesize hard samples at the feature level. By masking
dominant tokens and erasing principal channels, we generate hard samples to
facilitate learning representations with better discrimination and
generalization performance. Extensive experiments show that our PointCMP
achieves the state-of-the-art performance on benchmark datasets and outperforms
existing full-supervised counterparts. Transfer learning results demonstrate
the superiority of the learned representations across different datasets and
tasks.