  Self-supervised learning (SSL) is a technique for learning useful
representations from unlabeled data. It has been applied effectively to domain
adaptation (DA) on images and videos. It is still unknown if and how it can be
leveraged for domain adaptation in 3D perception problems. Here we describe the
first study of SSL for DA on point clouds. We introduce a new family of pretext
tasks, Deformation Reconstruction, inspired by the deformations encountered in
sim-to-real transformations. In addition, we propose a novel training procedure
for labeled point cloud data motivated by the MixUp method called Point cloud
Mixup (PCM). Evaluations on domain adaptations datasets for classification and
segmentation, demonstrate a large improvement over existing and baseline
methods.
