PointCMT: An MLP-Transformer Network for Contrastive Learning of Point Representation.

The irregular and unordered structure makes deep learning based point cloud analysis become a challenging task. Although the recent success of Transformer frameworks in Natural Language Processing (NLP) leads to a series of key advances in dealing with point cloud, most approaches in this category mainly focus on global information representation. In this paper, we propose a Point based MLP-Transformer Contrastive Learning architecture, termed as PointCMT. Specifically, by the introduction of the key ideas and advantages behind the MLP and Transformer, a locally Multi-scale Contrastive Feature Representation Learning module is developed as the core component of our network, which involves a local neighborhood embedding layer, and a contrastive learning model with our well-designed MLP-Transformer feature extractor (MFormer) as fundamental blocks. Experimental evidences on public benchmark datasets indicate that, compared with most state-of-the-arts, our proposed framework can obtain competitive or even better performance in terms of 3D point cloud analysis tasks. The code and trained models will be available at Github.