  In this paper, we propose a simple and general framework for self-supervised
point cloud representation learning. Human beings understand the 3D world by
extracting two levels of information and establishing the relationship between
them. One is the global shape of an object, and the other is the local
structures of it. However, few existing studies in point cloud representation
learning explored how to learn both global shapes and local-to-global
relationships without a specified network architecture. Inspired by how human
beings understand the world, we utilize knowledge distillation to learn both
global shape information and the relationship between global shape and local
structures. At the same time, we combine contrastive learning with knowledge
distillation to make the teacher network be better updated. Our method achieves
the state-of-the-art performance on linear classification and multiple other
downstream tasks. Especially, we develop a variant of ViT for 3D point cloud
feature extraction, which also achieves comparable results with existing
backbones when combined with our framework, and visualization of the attention
maps show that our model does understand the point cloud by combining the
global shape information and multiple local structural information, which is
consistent with the inspiration of our representation learning method. Our code
will be released soon.
