Unsupervised 3D Point Cloud Completion via Multi-View Adversarial Learning
In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues. The tasks of self-supervised and weakly-supervised point cloud completion involve reconstructing missing regions of these incomplete objects without the supervision of complete ground truth. Current methods e...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 31(2025), 10 vom: 02. Sept., Seite 7890-7905
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| 1. Verfasser: |
Wu, Lintai
(VerfasserIn) |
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| Weitere Verfasser: |
Cheng, Xianjing,
Xu, Yong,
Zeng, Huanqiang,
Hou, Junhui |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2025
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics
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| Schlagworte: | Journal Article |
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