PointJEM: Self-supervised Point Cloud Understanding for Reducing Feature
  Redundancy via Joint Entropy Maximization
Xin Cao Huan Xia Xinxin Han Yifan Wang Kang Li Linzhi Su
Most deep learning-based point cloud processing methods are supervised and
require large scale of labeled data. However, manual labeling of point cloud
data is laborious and time-consuming. Self-supervised representation learning
can address the aforementioned issue by learning robust and generalized
representations from unlabeled datasets. Nevertheless, the embedded features
obtained by representation learning usually contain redundant information, and
most current methods reduce feature redundancy by linear correlation
constraints. In this paper, we propose PointJEM, a self-supervised
representation learning method applied to the point cloud field. PointJEM
comprises an embedding scheme and a loss function based on joint entropy. The
embedding scheme divides the embedding vector into different parts, each part
can learn a distinctive feature. To reduce redundant information in the
features, PointJEM maximizes the joint entropy between the different parts,
thereby rendering the learned feature variables pairwise independent. To
validate the effectiveness of our method, we conducted experiments on multiple
datasets. The results demonstrate that our method can significantly reduce
feature redundancy beyond linear correlation. Furthermore, PointJEM achieves
competitive performance in downstream tasks such as classification and
segmentation.