Self-Supervised Learning on Point Clouds by Contrasting Global and Local Features

Traditional supervised learning methods rely on large amounts of labeled data,while collecting labeled data with high quality is expensive.To this end,a self-supervised learning method based on contrasting global and local features of point clouds is proposed,which includes two stages:data construction and contrastive learning.In the data construction phase,local regions are generated by partial scanning from different views and cropping local substructures.In the contrastive learning phase,the global object and the local region are sent to the encoder,the projection layer and the predictor in sequence to get the global and local features respectively,and the objective function based on the contrastive learning is used to enhance the similarity between global and local features.The experimental results on two public datasets including Model Net40 and ShapeNet show that compared with other methods such as Info3D,the proposed method can significantly improve the classification accuracy in both unsupervised point cloud classification and few-shot learning tasks,and it is more robust than existing methods when training data is scarce.