CVPR 2023 – PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos

In this episode we discuss PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos
by Zhiqiang Shen, Xiaoxiao Sheng, Longguang Wang, Yulan Guo, Qiong Liu, Xi Zhou. The paper proposed a self-supervised learning framework, called PointCMP, for point cloud videos, in which high labeling costs make unsupervised methods appealing. PointCMP uses a two-branch structure to simultaneously learn local and global spatio-temporal information. The framework includes a mutual similarity-based augmentation module to generate hard samples for better discrimination and generalization performance, resulting in state-of-the-art performance on benchmark datasets and outperforming fully-supervised methods. Transfer learning experiments also demonstrate the superior quality of representations learned with PointCMP.