ML Anthology

Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification

Erik Roeland van Dop, Paul P. L. Regtien
CVPR 1998 pp. 396-401
doi:10.1109/CVPR.1998.698636 /cvpr/1998/vandop1998cvpr-fitting/

Abstract

Undeformed superquadrics are volumetric modeling primitives with an extensive shape vocabulary that are described by only 5 parameters. Fitting these models viewpoint invariantly to range data enables classification based on the superquadric parameters. However, current recovery routines show several limitations, especially when the algorithms are applied to range images instead of true 3D images. In this paper problems with the common superquadric recovery procedure are identified and solutions are presented

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Cite

Text

van Dop and Regtien. "Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998. doi:10.1109/CVPR.1998.698636

Markdown

[van Dop and Regtien. "Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998.](https://mlanthology.org/cvpr/1998/vandop1998cvpr-fitting/) doi:10.1109/CVPR.1998.698636

BibTeX

@inproceedings{vandop1998cvpr-fitting,
  title     = {{Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification}},
  author    = {van Dop, Erik Roeland and Regtien, Paul P. L.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1998},
  pages     = {396-401},
  doi       = {10.1109/CVPR.1998.698636},
  url       = {https://mlanthology.org/cvpr/1998/vandop1998cvpr-fitting/}
}