A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
For networks that process 3D data, estimating the orientation and position of 3D objects is a challenging task. This is because the traditional networks are not robust to the rotation of the data, and their internal workings are largely opaque and uninterpretable. To solve this problem, a novel equi...
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| 主要な著者: | , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI AG
2025-03-01
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| シリーズ: | Algorithms |
| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/1999-4893/18/3/152 |
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