  Though a number of point cloud learning methods have been proposed to handle
unordered points, most of them are supervised and require labels for training.
By contrast, unsupervised learning of point cloud data has received much less
attention to date. In this paper, we propose a simple yet effective approach
for unsupervised point cloud learning. In particular, we identify a very useful
transformation which generates a good contrastive version of an original point
cloud. They make up a pair. After going through a shared encoder and a shared
head network, the consistency between the output representations are maximized
with introducing two variants of contrastive losses to respectively facilitate
downstream classification and segmentation. To demonstrate the efficacy of our
method, we conduct experiments on three downstream tasks which are 3D object
classification (on ModelNet40 and ModelNet10), shape part segmentation (on
ShapeNet Part dataset) as well as scene segmentation (on S3DIS). Comprehensive
results show that our unsupervised contrastive representation learning enables
impressive outcomes in object classification and semantic segmentation. It
generally outperforms current unsupervised methods, and even achieves
comparable performance to supervised methods. Our source codes will be made
publicly available.
