  Learning from unlabeled or partially labeled data to alleviate human labeling
remains a challenging research topic in 3D modeling. Along this line,
unsupervised representation learning is a promising direction to auto-extract
features without human intervention. This paper proposes a general unsupervised
approach, named \textbf{ConClu}, to perform the learning of point-wise and
global features by jointly leveraging point-level clustering and instance-level
contrasting. Specifically, for one thing, we design an Expectation-Maximization
(EM) like soft clustering algorithm that provides local supervision to extract
discriminating local features based on optimal transport. We show that this
criterion extends standard cross-entropy minimization to an optimal transport
problem, which we solve efficiently using a fast variant of the Sinkhorn-Knopp
algorithm. For another, we provide an instance-level contrasting method to
learn the global geometry, which is formulated by maximizing the similarity
between two augmentations of one point cloud. Experimental evaluations on
downstream applications such as 3D object classification and semantic
segmentation demonstrate the effectiveness of our framework and show that it
can outperform state-of-the-art techniques.
