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, which can be solved by using an efficient variant of the Sinkhorn-Knopp algorithm. We formulate an unsupervised loss to minimize the standard cross-entropy between pseudo-labels and predicted labels.