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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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - PP(2024) vom: 05. Sept.
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| 1. Verfasser: |
Zhao, Bao
(VerfasserIn) |
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| Weitere Verfasser: |
Liu, Qiang,
Wang, Zihan,
Chen, Xiaobo,
Jia, Zhaohong,
Liang, Dong |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2024
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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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