ML Anthology

Using Hyperquadrics for Shape Recovery from Range Data

Song Han, Dmitry B. Goldgof, Kevin W. Bowyer
ICCV 1993 pp. 492-496
doi:10.1109/ICCV.1993.378174 /iccv/1993/han1993iccv-using/

Abstract

Superquadric is an implicit model which was recently introduced and successfully applied in computer vision research. The authors introduce its generalization, the use of the hyperquadric models, for computer vision applications, and focus on its utilization for shape recovery from range data. The hyperquadric model can be composed of any number of terms. Its geometric bound is an arbitrary convex polyhedron, and thus it can describe more complex shapes than the superquadric. A fitting method is proposed that starts with a rough fit with only two terms in the 2-D case or three terms in the 3-D case, and then adds additional terms to improve the fit. The experiments indicate that the use of hyperquadrics is a promising paradigm for shape representation and recovery in computer vision.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Han et al. "Using Hyperquadrics for Shape Recovery from Range Data." IEEE/CVF International Conference on Computer Vision, 1993. doi:10.1109/ICCV.1993.378174

Markdown

[Han et al. "Using Hyperquadrics for Shape Recovery from Range Data." IEEE/CVF International Conference on Computer Vision, 1993.](https://mlanthology.org/iccv/1993/han1993iccv-using/) doi:10.1109/ICCV.1993.378174

BibTeX

@inproceedings{han1993iccv-using,
  title     = {{Using Hyperquadrics for Shape Recovery from Range Data}},
  author    = {Han, Song and Goldgof, Dmitry B. and Bowyer, Kevin W.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1993},
  pages     = {492-496},
  doi       = {10.1109/ICCV.1993.378174},
  url       = {https://mlanthology.org/iccv/1993/han1993iccv-using/}
}