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...
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Détails bibliographiques
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1996. - 28(2022), 12 vom: 25. Dez., Seite 4503-4514
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| Auteur principal: |
Li, Xianzhi
(Auteur) |
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| Autres auteurs: |
Li, Ruihui,
Chen, Guangyong,
Fu, Chi-Wing,
Cohen-Or, Daniel,
Heng, Pheng-Ann |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2022
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| Accès à la collection: | IEEE transactions on visualization and computer graphics
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| Sujets: | Journal Article |
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