  Point cloud classification is a popular task in 3D vision. However, previous
works, usually assume that point clouds at test time are obtained with the same
procedure or sensor as those at training time. Unsupervised Domain Adaptation
(UDA) instead, breaks this assumption and tries to solve the task on an
unlabeled target domain, leveraging only on a supervised source domain. For
point cloud classification, recent UDA methods try to align features across
domains via auxiliary tasks such as point cloud reconstruction, which however
do not optimize the discriminative power in the target domain in feature space.
In contrast, in this work, we focus on obtaining a discriminative feature space
for the target domain enforcing consistency between a point cloud and its
augmented version. We then propose a novel iterative self-training methodology
that exploits Graph Neural Networks in the UDA context to refine pseudo-labels.
We perform extensive experiments and set the new state-of-the-art in standard
UDA benchmarks for point cloud classification. Finally, we show how our
approach can be extended to more complex tasks such as part segmentation.
