Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views

@article{Wei2021LearningCV,
  title={Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views},
  author={Xin Wei and Yifei Gong and Fudong Wang and Xing Sun and Jian Sun},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={397-406}
}
  • Xin Wei, Yifei Gong, Jian Sun
  • Published 16 August 2021
  • Computer Science
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
In this paper, we focus on recognizing 3D shapes from arbitrary views, i.e., arbitrary numbers and positions of viewpoints. It is a challenging and realistic setting for view-based 3D shape recognition. We propose a canonical view representation to tackle this challenge. We first transform the original features of arbitrary views to a fixed number of view features, dubbed canonical view representation, by aligning the arbitrary view features to a set of learnable reference view features using… 

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