https://scholars.lib.ntu.edu.tw/handle/123456789/632091
標題: | CF-NET: COMPLEMENTARY FUSION NETWORK FOR ROTATION INVARIANT POINT CLOUD COMPLETION | 作者: | Chen B.-F Yeh Y.-M YI-CHANG LU |
關鍵字: | Deep learning; Point cloud completion; Rotation invariant | 公開日期: | 2022 | 卷: | 2022-May | 起(迄)頁: | 2275-2279 | 來源出版物: | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | 摘要: | Real-world point clouds usually have inconsistent orientations and often suffer from data missing issues. To solve this problem, we design a neural network, CF-Net, to address challenges in rotation invariant completion. In our network, we modify and integrate complementary operators to extract features that are robust against rotation and incompleteness. Our CF-Net can achieve competitive results both geometrically and semantically as demonstrated in this paper. © 2022 IEEE |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131229519&doi=10.1109%2fICASSP43922.2022.9746388&partnerID=40&md5=a2910df721876c6c113132323d8265b1 https://scholars.lib.ntu.edu.tw/handle/123456789/632091 |
ISSN: | 15206149 | DOI: | 10.1109/ICASSP43922.2022.9746388 |
顯示於: | 電機工程學系 |
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