Non-rigid 3D Shape Registration using an Adaptive Template

@article{Dai2018Nonrigid3S,
  title={Non-rigid 3D Shape Registration using an Adaptive Template},
  author={Hang Dai and Nick Pears and William Smith},
  journal={ArXiv},
  year={2018},
  volume={abs/1803.07973}
}
  • Hang Dai, Nick Pears, William Smith
  • Published in ECCV Workshops 2018
  • Computer Science
  • ArXiv
  • We present a new fully-automatic non-rigid 3D shape registration (morphing) framework comprising (1) a new 3D landmarking and pose normalisation method; (2) an adaptive shape template method to accelerate the convergence of registration algorithms and achieve a better final shape correspondence and (3) a new iterative registration method that combines Iterative Closest Points with Coherent Point Drift (CPD) to achieve a more stable and accurate correspondence establishment than standard CPD. We… CONTINUE READING

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    Citations

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    PointAE: Point Auto-Encoder for 3D Statistical Shape and Texture Modelling

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    • Computer Science
    • 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
    • 2019
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    Statistical modelling of craniofacial shape

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