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CF-NET: COMPLEMENTARY FUSION NETWORK FOR ROTATION INVARIANT POINT CLOUD COMPLETION

Citation Author(s):
Bo-Fan Chen, Yang-Ming Yeh, Yi-Chang Lu
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

Abstract

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.

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