Learnable Motion Coherence for Correspondence Pruning

@article{Liu2021LearnableMC,
  title={Learnable Motion Coherence for Correspondence Pruning},
  author={Yuan Liu and Lingjie Liu and Chu-Hsing Lin and Zhen Dong and Wenping Wang},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={3236-3245}
}
  • Yuan Liu, Lingjie Liu, +2 authors Wenping Wang
  • Published 30 November 2020
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to their sparsity and uneven distributions. Existing works on motion coherence are sensitive to parameter settings and have difficulty in dealing with complex motion patterns. In this paper, we introduce a network called Laplacian Motion Coherence Network (LMCNet) to learn motion coherence property for correspondence… Expand
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