PointJEM: Self-supervised Point Cloud Understanding for Reducing Feature Redundancy via Joint Entropy Maximization

• URL: http://arxiv.org/abs/2312.03339v1
• Date: Wed, 6 Dec 2023 08:21:42 GMT
• Title: PointJEM: Self-supervised Point Cloud Understanding for Reducing Feature Redundancy via Joint Entropy Maximization
• Authors: Xin Cao, Huan Xia, Xinxin Han, Yifan Wang, Kang Li, and Linzhi Su
• Abstract summary: We propose PointJEM, a self-supervised representation learning method applied to the point cloud field. To reduce redundant information in the features, PointJEM maximizes the joint entropy between the different parts. PointJEM achieves competitive performance in downstream tasks such as classification and segmentation.
• Score: 10.53900407467811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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