CF-NET: COMPLEMENTARY FUSION NETWORK FOR ROTATION INVARIANT POINT CLOUD COMPLETION
Bo-Fan Chen, Yang-Ming Yeh, Yi-Chang Lu
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10 May 2022
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.
