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.

PointJEM 是一种自我监督表示学习方法，通过最大化不同部分之间的联合熵，减少点云领域嵌入特征的冗余信息，实现学习到的特征变量成对独立，从而显著减少特征的冗余，并在分类和分割等下游任务中取得了有竞争力的性能。