{"authors":[{"id":null,"fullName":"Ren, Bin","name":"Bin","surname":"Ren","rank":1,"pid":null},{"id":null,"fullName":"Huang, Xiaoshui","name":"Xiaoshui","surname":"Huang","rank":2,"pid":null},{"id":null,"fullName":"Liu, Mengyuan","name":"Mengyuan","surname":"Liu","rank":3,"pid":null},{"id":null,"fullName":"Liu, Hong","name":"Hong","surname":"Liu","rank":4,"pid":null},{"id":null,"fullName":"Poiesi, Fabio","name":"Fabio","surname":"Poiesi","rank":5,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0002-9769-1279"},"provenance":null}},{"id":"orcid_______::759d3ab6618d00adbdd97e20faae993c","fullName":"Sebe, Niculae","name":"Niculae","surname":"Sebe","rank":6,"pid":{"id":{"scheme":"orcid","value":"0000-0002-6597-7248"},"provenance":null}},{"id":null,"fullName":"Mei, Guofeng","name":"Guofeng","surname":"Mei","rank":7,"pid":null}],"openAccessColor":null,"publiclyFunded":false,"eoscIfGuidelines":null,"type":"publication","language":{"code":"und","label":"Undetermined"},"countries":[{"code":"IT","label":"Italy","provenance":null}],"subjects":[{"subject":{"scheme":"keyword","value":"FOS: Computer and information sciences"},"provenance":null},{"subject":{"scheme":"keyword","value":"Computer Vision and Pattern Recognition (cs.CV)"},"provenance":null},{"subject":{"scheme":"FOS","value":"0202 electrical engineering, electronic engineering, information engineering"},"provenance":null},{"subject":{"scheme":"FOS","value":"02 engineering and technology"},"provenance":null},{"subject":{"scheme":"keyword","value":"Computer Vision and Pattern Recognition"},"provenance":null}],"mainTitle":"Masked Clustering Prediction for Unsupervised Point Cloud Pre-training","subTitle":null,"descriptions":["<jats:p>Vision transformers (ViTs) have recently been widely applied to 3D point cloud understanding, with masked autoencoding as the predominant pre-training paradigm. However, the challenge of learning dense and informative semantic features from point clouds via standard ViTs remains underexplored. We propose MaskClu, a novel unsupervised pre-training method for ViTs on 3D point clouds that integrates masked point modeling with clustering-based learning. MaskClu is designed to reconstruct both cluster assignments and cluster centers from masked point clouds, thus encouraging the model to capture dense semantic information. Additionally, we introduce a global contrastive learning mechanism that enhances instance-level feature learning by contrasting different masked views of the same point cloud. By jointly optimizing these complementary objectives, i.e., dense semantic reconstruction, and instance-level contrastive learning. MaskClu enables ViTs to learn richer and more semantically meaningful representations from 3D point clouds. 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