HA-TiNet : Learning a Distinctive and General 3D Local Descriptor for Point Cloud Registration
Extracting geometric features from 3D point clouds is widely applied in many tasks, including registration and recognition. We propose a simple yet effective method, termed height-azimuth image based transformation-invariant net (HA-TiNet), to learn a distinctive, general and rotation-invariant 3D l...
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Détails bibliographiques
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1996. - 31(2025), 9 vom: 01. Aug., Seite 5507-5518
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| Auteur principal: |
Zhao, Bao
(Auteur) |
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| Autres auteurs: |
Liu, Qiang,
Wang, Zihan,
Chen, Xiaobo,
Jia, Zhaohong,
Liang, Dong |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2025
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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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