  Large and rich data is a prerequisite for effective training of deep neural
networks. However, the irregularity of point cloud data makes manual annotation
time-consuming and laborious. Self-supervised representation learning, which
leverages the intrinsic structure of large-scale unlabelled data to learn
meaningful feature representations, has attracted increasing attention in the
field of point cloud research. However, self-supervised representation learning
often suffers from model collapse, resulting in reduced information and
diversity of the learned representation, and consequently degrading the
performance of downstream tasks. To address this problem, we propose
PointMoment, a novel framework for point cloud self-supervised representation
learning that utilizes a high-order mixed moment loss function rather than the
conventional contrastive loss function. Moreover, our framework does not
require any special techniques such as asymmetric network architectures,
gradient stopping, etc. Specifically, we calculate the high-order mixed moment
of the feature variables and force them to decompose into products of their
individual moment, thereby making multiple variables more independent and
minimizing the feature redundancy. We also incorporate a contrastive learning
approach to maximize the feature invariance under different data augmentations
of the same point cloud. Experimental results show that our approach
outperforms previous unsupervised learning methods on the downstream task of 3D
point cloud classification and segmentation.
