CF-Net: Complementary Fusion Network for Rotation Invariant Point Cloud Completion

Bo-Fan Chen; Yang-Ming Yeh; Yi-Chang Lu

ICASSP 2022accepted0 citations

CF-Net: Complementary Fusion Network for Rotation Invariant Point Cloud Completion

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.

BibTeX

@inproceedings{icassp2022_cfnetcomplementa,
  title = {CF-Net: Complementary Fusion Network for Rotation Invariant Point Cloud Completion},
  author = {Bo-Fan Chen and Yang-Ming Yeh and Yi-Chang Lu},
  booktitle = {ICASSP 2022},
  year = {2022}
}