Non-Rigid 3D Shape Registration Using an Adaptive Template

Hang Dai, Nick E. Pears, William A. P. Smith

ECCVW 2018 pp. 48-63

doi:10.1007/978-3-030-11018-5_5 /eccvw/2018/dai2018eccvw-nonrigid/

Abstract

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 call this new morphing approach Iterative Coherent Point Drift (ICPD). Our proposed framework is evaluated qualitatively and quantitatively on three datasets and compared with several other methods. The proposed framework is shown to give state-of-the-art performance.

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Cite

Text

Dai et al. "Non-Rigid 3D Shape Registration Using an Adaptive Template." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11018-5_5

Markdown

[Dai et al. "Non-Rigid 3D Shape Registration Using an Adaptive Template." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/dai2018eccvw-nonrigid/) doi:10.1007/978-3-030-11018-5_5

BibTeX

@inproceedings{dai2018eccvw-nonrigid,
  title     = {{Non-Rigid 3D Shape Registration Using an Adaptive Template}},
  author    = {Dai, Hang and Pears, Nick E. and Smith, William A. P.},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2018},
  pages     = {48-63},
  doi       = {10.1007/978-3-030-11018-5_5},
  url       = {https://mlanthology.org/eccvw/2018/dai2018eccvw-nonrigid/}
}