ML Anthology

Recovering Parametric Geons from Multiview Range Data

Kenong Wu, Martin D. Levine
CVPR 1994 pp. 159-166
doi:10.1109/CVPR.1994.323824 /cvpr/1994/wu1994cvpr-recovering/

Abstract

Focuses on approximating object part shapes by distinctive types of volumetric primitives. Shape approximation is accomplished by fitting volumetric models called 'parametric geons' to multiview range data of single-part objects and classifying the fitting residuals. Parametric geons are seven qualitative shape types defined by parameterized equations which control the size and degree of tapering and bending. Model fitting is performed by minimizing an objective function which measures the similarity in both size and shape between models and objects. Multiple view data, global shape constraints and global optimization are employed to obtain unique models and to compensate for noise and minor variations in object shape. This approach has been studied in experiments with both synthetic 3D data and actual rangefinder data of perfect and imperfect geon-like objects.< <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

Wu and Levine. "Recovering Parametric Geons from Multiview Range Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994. doi:10.1109/CVPR.1994.323824

Markdown

[Wu and Levine. "Recovering Parametric Geons from Multiview Range Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994.](https://mlanthology.org/cvpr/1994/wu1994cvpr-recovering/) doi:10.1109/CVPR.1994.323824

BibTeX

@inproceedings{wu1994cvpr-recovering,
  title     = {{Recovering Parametric Geons from Multiview Range Data}},
  author    = {Wu, Kenong and Levine, Martin D.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1994},
  pages     = {159-166},
  doi       = {10.1109/CVPR.1994.323824},
  url       = {https://mlanthology.org/cvpr/1994/wu1994cvpr-recovering/}
}