  Unsupervised learning on 3D point clouds has undergone a rapid evolution,
especially thanks to data augmentation-based contrastive methods. However, data
augmentation is not ideal as it requires a careful selection of the type of
augmentations to perform, which in turn can affect the geometric and semantic
information learned by the network during self-training. To overcome this
issue, we propose an augmentation-free unsupervised approach for point clouds
to learn transferable point-level features via soft clustering, named SoftClu.
SoftClu assumes that the points belonging to a cluster should be close to each
other in both geometric and feature spaces. This differs from typical
contrastive learning, which builds similar representations for a whole point
cloud and its augmented versions. We exploit the affiliation of points to their
clusters as a proxy to enable self-training through a pseudo-label prediction
task. Under the constraint that these pseudo-labels induce the equipartition of
the point cloud, we cast SoftClu as an optimal transport problem. We formulate
an unsupervised loss to minimize the standard cross-entropy between
pseudo-labels and predicted labels. Experiments on downstream applications,
such as 3D object classification, part segmentation, and semantic segmentation,
show the effectiveness of our framework in outperforming state-of-the-art
techniques.
