Contrastive Semantic-Aware Masked Autoencoder for Point Cloud Self-Supervised Learning

Masked Autoencoder (MAE) has shown remarkable potential in self-supervised representation learning for 3D point clouds. However, these methods primarily rely on point-level or low-level feature reconstruction, forcing the model to focus on local regions while lacking enough global discriminability i...

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Bibliographic Details
Published in:	IEEE signal processing letters Vol. 32; pp. 1760 - 1764
Main Authors:	He, Yuan, Hu, Guyue, Yu, Shan
Format:	Journal Article
Language:	English
Published:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	3D point cloud Artificial intelligence Autoencoders Brain Classification contrastive regularization Image reconstruction Machine learning masked autoencoder Masking masking strategy Nearest neighbor methods Point cloud compression Reconstruction Regularization Representations Self-supervised learning Semantics Shape Three dimensional models Three-dimensional displays
ISSN:	1070-9908, 1558-2361
Online Access:	Get full text
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