Regularizing 3D Medial Axis Using Medial Scaffold Transforms

Abstract

This paper addresses a key bottleneck in the use of the 3D medial axis (MA) representation, namely, how the complex MA structure can be regularized so that similar, within-category 3D shapes yield similar 3D MA that are distinct from the non-category shapes. We rely on previous work which (i) constructs a hierarchical MA hypergraph, the medial scaffold (MS), and (ii) the theoretical classification of the instabilities of this structure, or transitions (sudden topological changes due to a small perturbation). The shapes at transition point are degenerate. Our approach is to recognize the transitions which are close-by to a given shape and transform the shape to this transition point, and repeat until no close-by transitions exists. This move towards degeneracy is the basis of simplification of shape. We derive 11 transforms from 7 transitions and follow a greedy scheme in applying the transform. The results show that the simplified MA preserves with-in-category similarity, thus indicating its potential use in various applications including shape analysis, manipulation, and matching.

Cite

Text

Chang and Kimia. "Regularizing 3D Medial Axis Using Medial Scaffold Transforms." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587594

Markdown

[Chang and Kimia. "Regularizing 3D Medial Axis Using Medial Scaffold Transforms." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/chang2008cvpr-regularizing/) doi:10.1109/CVPR.2008.4587594

BibTeX

@inproceedings{chang2008cvpr-regularizing,
  title     = {{Regularizing 3D Medial Axis Using Medial Scaffold Transforms}},
  author    = {Chang, Ming-Ching and Kimia, Benjamin B.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2008},
  doi       = {10.1109/CVPR.2008.4587594},
  url       = {https://mlanthology.org/cvpr/2008/chang2008cvpr-regularizing/}
}