  Deep neural networks have achieved promising performance in supervised point
cloud applications, but manual annotation is extremely expensive and
time-consuming in supervised learning schemes. Unsupervised domain adaptation
(UDA) addresses this problem by training a model with only labeled data in the
source domain but making the model generalize well in the target domain.
Existing studies show that self-supervised learning using both source and
target domain data can help improve the adaptability of trained models, but
they all rely on hand-crafted designs of the self-supervised tasks. In this
paper, we propose a learnable self-supervised task and integrate it into a
self-supervision-based point cloud UDA architecture. Specifically, we propose a
learnable nonlinear transformation that transforms a part of a point cloud to
generate abundant and complicated point clouds while retaining the original
semantic information, and the proposed self-supervised task is to reconstruct
the original point cloud from the transformed ones. In the UDA architecture, an
encoder is shared between the networks for the self-supervised task and the
main task of point cloud classification or segmentation, so that the encoder
can be trained to extract features suitable for both the source and the target
domain data. Experiments on PointDA-10 and PointSegDA datasets show that the
proposed method achieves new state-of-the-art performance on both
classification and segmentation tasks of point cloud UDA. Code will be made
publicly available.
