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3D Open-Surface Shape Correspondence for Statistical Shape Modeling: Identifying Topologically Consistent Landmarks

Pahal Dalal, Lili Ju, Michael McLaughlin, Xiangrong Zhou, Hiroshi Fujita, Song Wang
ICCV 2009 pp. 1857-1864
doi:10.1109/ICCV.2009.5459412 /iccv/2009/dalal2009iccv-d/

Abstract

Shape correspondence, which aims at accurately identifying corresponding landmarks from a given population of shape instances, is a very challenging step in constructing a statistical shape model such as the Point Distribution Model. The state-of-the-art methods such as MDL and SPHARM are primarily focused on closed-surface shape correspondence. In this paper we develop a novel method aimed at identifying accurately corresponding landmarks on 3D open-surfaces with a closed boundary. In particular, we enforce explicit topology consistency on the identified landmarks to ensure that they form a simple, consistent triangle mesh to more accurately model the correspondence of the underlying continuous shape instances. The proposed method also ensures the correspondence of the boundary of the open surfaces. For our experiments, we test the proposed method by constructing a statistical shape model of the human diaphragm from 26 shape instances.

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Cite

Text

Dalal et al. "3D Open-Surface Shape Correspondence for Statistical Shape Modeling: Identifying Topologically Consistent Landmarks." IEEE/CVF International Conference on Computer Vision, 2009. doi:10.1109/ICCV.2009.5459412

Markdown

[Dalal et al. "3D Open-Surface Shape Correspondence for Statistical Shape Modeling: Identifying Topologically Consistent Landmarks." IEEE/CVF International Conference on Computer Vision, 2009.](https://mlanthology.org/iccv/2009/dalal2009iccv-d/) doi:10.1109/ICCV.2009.5459412

BibTeX

@inproceedings{dalal2009iccv-d,
  title     = {{3D Open-Surface Shape Correspondence for Statistical Shape Modeling: Identifying Topologically Consistent Landmarks}},
  author    = {Dalal, Pahal and Ju, Lili and McLaughlin, Michael and Zhou, Xiangrong and Fujita, Hiroshi and Wang, Song},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2009},
  pages     = {1857-1864},
  doi       = {10.1109/ICCV.2009.5459412},
  url       = {https://mlanthology.org/iccv/2009/dalal2009iccv-d/}
}