CrossAlignNet: a self-supervised feature learning framework for 3D point cloud understanding Article Swipe
Fei Wang
,
Xinglong Dong
,
Jia Wu
,
Weishi Zhang
,
Tuo Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.3194
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.3194
We propose a self-supervised point cloud representation learning framework CrossAlignNet based on cross-modal mask alignment strategy, to solve the problems of imbalance between global semantic and local geometric feature learning, as well as cross-modal information asymmetry in existing methods. A geometrically consistent mask region is established between the point cloud patches and the corresponding image patches through a synchronized mask alignment strategy to ensure cross-modal information symmetry. A dual-task learning framework is designed: the global semantic alignment task enhances the cross-modal semantic consistency through contrastive learning, and the local mask reconstruction task fuses the image cues using the cross-attention mechanism to recover the local geometric structure of the masked point cloud. In addition, the ShapeNet3D-CMA dataset is constructed to provide accurate point cloud-image spatial mapping relations to support cross-modal learning. Our framework shows superior or comparative results against existing methods on three point cloud understanding tasks including object classification, few-shot classification, and part segmentation.
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CrossAlignNet: a self-supervised feature learning framework for 3D point cloud understandingWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-09-19Full publication date if available
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Fei Wang, Xinglong Dong, Jia Wu, Weishi Zhang, Tuo ZhouList of authors in order
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https://doi.org/10.7717/peerj-cs.3194Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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| abstract_inverted_index.support | 127 |
| abstract_inverted_index.through | 56, 83 |
| abstract_inverted_index.accurate | 120 |
| abstract_inverted_index.enhances | 78 |
| abstract_inverted_index.existing | 37, 138 |
| abstract_inverted_index.few-shot | 149 |
| abstract_inverted_index.learning | 7, 69 |
| abstract_inverted_index.methods. | 38 |
| abstract_inverted_index.problems | 19 |
| abstract_inverted_index.semantic | 24, 75, 81 |
| abstract_inverted_index.strategy | 61 |
| abstract_inverted_index.superior | 133 |
| abstract_inverted_index.addition, | 112 |
| abstract_inverted_index.alignment | 14, 60, 76 |
| abstract_inverted_index.asymmetry | 35 |
| abstract_inverted_index.designed: | 72 |
| abstract_inverted_index.dual-task | 68 |
| abstract_inverted_index.framework | 8, 70, 131 |
| abstract_inverted_index.geometric | 27, 104 |
| abstract_inverted_index.imbalance | 21 |
| abstract_inverted_index.including | 146 |
| abstract_inverted_index.learning, | 29, 85 |
| abstract_inverted_index.learning. | 129 |
| abstract_inverted_index.mechanism | 99 |
| abstract_inverted_index.relations | 125 |
| abstract_inverted_index.strategy, | 15 |
| abstract_inverted_index.structure | 105 |
| abstract_inverted_index.symmetry. | 66 |
| abstract_inverted_index.consistent | 41 |
| abstract_inverted_index.cloud-image | 122 |
| abstract_inverted_index.comparative | 135 |
| abstract_inverted_index.consistency | 82 |
| abstract_inverted_index.constructed | 117 |
| abstract_inverted_index.contrastive | 84 |
| abstract_inverted_index.cross-modal | 12, 33, 64, 80, 128 |
| abstract_inverted_index.established | 45 |
| abstract_inverted_index.information | 34, 65 |
| abstract_inverted_index.synchronized | 58 |
| abstract_inverted_index.CrossAlignNet | 9 |
| abstract_inverted_index.corresponding | 53 |
| abstract_inverted_index.geometrically | 40 |
| abstract_inverted_index.segmentation. | 153 |
| abstract_inverted_index.understanding | 144 |
| abstract_inverted_index.ShapeNet3D-CMA | 114 |
| abstract_inverted_index.reconstruction | 90 |
| abstract_inverted_index.representation | 6 |
| abstract_inverted_index.classification, | 148, 150 |
| abstract_inverted_index.cross-attention | 98 |
| abstract_inverted_index.self-supervised | 3 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.46451307 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |