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CF-NET: COMPLEMENTARY FUSION NETWORK FOR ROTATION INVARIANT POINT CLOUD COMPLETION
![](https://sigport.org/sites/default/files/styles/home/public/%E8%9E%A2%E5%B9%95%E6%93%B7%E5%8F%96%E7%95%AB%E9%9D%A2%202022-05-08%20165749_0.png?itok=SxtYuFWZ)
- Citation Author(s):
- Submitted by:
- Yang-Ming Yeh
- Last updated:
- 8 May 2022 - 10:30am
- Document Type:
- Presentation Slides
- Document Year:
- 2022
- Event:
- Presenters:
- Yang-Ming Yeh
- Paper Code:
- IVMSP-23.4
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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.