PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos (CVPR2023)

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.

The code is tested with Python 3.7.12, PyTorch 1.7.1, GCC 9.4.0, and CUDA 10.2. Compile the CUDA layers for PointNet++:

cd modules
python setup.py install

We thank the authors of related repositories:

1. PSTNet: https://github.com/hehefan/Point-Spatio-Temporal-Convolution
2. P4Transformer: https://github.com/hehefan/P4Transformer