ARTICLE INFO  
1. Introduction  
2. Background  
2.1. Self-supervised learning in the language and image domain  
2.2. Properties of the point cloud data  
2.3. Point Cloud Dataset  
2.4. Point cloud data augmentation  
2.5. Popular deep models for point clouds  
2.6. Pseudo labels  
2.7. Loss functions  
3. Self-Supervised Learning pretext tasks for point cloud  
3.1. Reconstruction-based methods  
3.2. Contrast-based methods  
3.3. Alignment-based methods  
3.4. Motion-based methods  
4. Downstream tasks  
4.1. Object classification  
4.2. Part segmentation  
4.3. Semantic segmentation  
4.4. Object detection  
5. Future directions  
5.1. Few-shot and zero-shot learning  
5.2. Multiple modality interaction and fusion  
5.3. Hierarchical feature extraction  
5.4. Multiple tasks pre-training  
5.5. Theory and interpretability  
6. Conclusion