A Rotation-Invariant Framework for Deep Point Cloud Analysis
Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations. In this article, we introduce a new low-level purely rotation-invariant representation to replace...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 28(2022), 12 vom: 25. Dez., Seite 4503-4514
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| 1. Verfasser: |
Li, Xianzhi
(VerfasserIn) |
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| Weitere Verfasser: |
Li, Ruihui,
Chen, Guangyong,
Fu, Chi-Wing,
Cohen-Or, Daniel,
Heng, Pheng-Ann |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2022
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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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