ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning

@article{Hanocka2018ALIGNetPA,
  title={ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning},
  author={Rana Hanocka and Noa Fish and Zhenhua Wang and Raja Giryes and Shachar Fleishman and Daniel Cohen-Or},
  journal={ArXiv},
  year={2018},
  volume={abs/1804.08497}
}
  • Rana Hanocka, Noa Fish, +3 authors Daniel Cohen-Or
  • Published in TOGS 2018
  • Mathematics, Computer Science
  • The process of aligning a pair of shapes is a fundamental operation in computer graphics. Traditional approaches rely heavily on matching corresponding points or features to guide the alignment, a paradigm that falters when significant shape portions are missing. These techniques generally do not incorporate prior knowledge about expected shape characteristics, which can help compensate for any misleading cues left by inaccuracies exhibited in the input shapes. We present an approach based on a… CONTINUE READING

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