Point clouds provide a compact and efficient representation of 3D shapes.
While deep neural networks have achieved impressive results on point cloud
learning tasks, they require massive amounts of manually labeled data, which
can be costly and time-consuming to collect. In this paper, we leverage 3D
self-supervision for learning downstream tasks on point clouds with fewer
labels. A point cloud can be rotated in infinitely many ways, which provides a
rich label-free source for self-supervision. We consider the auxiliary task of
predicting rotations that in turn leads to useful features for other tasks such
as shape classification and 3D keypoint prediction. Using experiments on
ShapeNet and ModelNet, we demonstrate that our approach outperforms the
state-of-the-art. Moreover, features learned by our model are complementary to
other self-supervised methods and combining them leads to further performance
improvement.

本文提出了一种基于 3D 自监督学习的点云学习方法，通过旋转点云实现无标签的自我监督，在形状分类和 3D 关键点预测等任务上表现出色，学习到的特征优于其他自监督方法。

通过方向估计进行点云的自监督学习

Self-supervised Learning of Point Clouds via Orientation Estimation

In this paper, we introduce a novel unsupervised domain adaptation technique
for the task of 3D keypoint prediction from a single depth scan or image. Our
key idea is to utilize the fact that predictions from different views of the
same or similar objects should be consistent with each other. Such view
consistency can provide effective regularization for keypoint prediction on
unlabeled instances. In addition, we introduce a geometric alignment term to
regularize predictions in the target domain. The resulting loss function can be
effectively optimized via alternating minimization. We demonstrate the
effectiveness of our approach on real datasets and present experimental results
showing that our approach is superior to state-of-the-art general-purpose
domain adaptation techniques.

本文提出了一种新颖的无监督领域自适应技术，用于从单个深度扫描或图像预测 3D 关键点，利用视角一致性和几何对齐等方法提高预测准确度，实验结果表明该方法胜于现有的领域自适应技术。