  Recently 3D point cloud learning has been a hot topic in computer vision and
autonomous driving. Due to the fact that it is difficult to manually annotate a
qualitative large-scale 3D point cloud dataset, unsupervised domain adaptation
(UDA) is popular in 3D point cloud learning which aims to transfer the learned
knowledge from the labeled source domain to the unlabeled target domain.
However, the generalization and reconstruction errors caused by domain shift
with simply-learned model are inevitable which substantially hinder the model's
capability from learning good representations. To address these issues, we
propose an end-to-end self-ensembling network (SEN) for 3D point cloud domain
adaption tasks. Generally, our SEN resorts to the advantages of Mean Teacher
and semi-supervised learning, and introduces a soft classification loss and a
consistency loss, aiming to achieve consistent generalization and accurate
reconstruction. In SEN, a student network is kept in a collaborative manner
with supervised learning and self-supervised learning, and a teacher network
conducts temporal consistency to learn useful representations and ensure the
quality of point clouds reconstruction. Extensive experiments on several 3D
point cloud UDA benchmarks show that our SEN outperforms the state-of-the-art
methods on both classification and segmentation tasks. Moreover, further
analysis demonstrates that our SEN also achieves better reconstruction results.
