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.

PointMoment是一种用于点云自监督表示学习的新框架，它利用高阶混合矩损失函数而不是传统的对比损失函数，计算特征变量的高阶混合矩并强制其分解为各自矩的乘积，从而使多个变量更加独立并最小化特征的冗余。该方法还结合了对比学习方法，用于最大化相同点云的不同数据增强下的特征不变性。实验结果表明，PointMoment在3D点云分类和分割等下游任务上优于先前的无监督学习方法。

PointMoment：基于混合矩的自监督表示学习3D点云