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Shape segmentation using local slippage analysis
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Source ACM International Conference Proceeding Series; Vol. 71 archive
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing table of contents
Nice, France
SESSION: Session 7 table of contents
Pages: 214 - 223  
Year of Publication: 2004
ISBN ~ ISSN:1727-8384 , 3-905673-13-4
Authors
Natasha Gelfand  Stanford University
Leonidas J. Guibas  Stanford University
Sponsor
Eurographics: Eurographics Association
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 8,   Downloads (12 Months): 37,   Citation Count: 15
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ABSTRACT

We propose a method for segmentation of 3D scanned shapes into simple geometric parts. Given an input point cloud, our method computes a set of components which possess one or more slippable motions: rigid motions which, when applied to a shape, slide the transformed version against the stationary version without forming any gaps. Slippable shapes include rotationally and translationally symmetrical shapes such as planes, spheres, and cylinders, which are often found as components of scanned mechanical parts. We show how to determine the slippable motions of a given shape by computing eigenvalues of a certain symmetric matrix derived from the points and normals of the shape. Our algorithm then discovers slippable components in the input data by computing local slippage signatures at a set of points of the input and iteratively aggregating regions with matching slippable motions. We demonstrate the performance of our algorithm for reverse engineering surfaces of mechanical parts.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY  15


REVIEW

"Joseph J. O'Rourke : Reviewer"

Given an unstructured set of points on the surface of a three-dimensional (3D) object, perhaps generated by a laser scan of a mechanical part, it is a challenging problem to reverse engineer the data to a computer-aided design (CAD) model.

more...

Collaborative Colleagues:
Natasha Gelfand: colleagues
Leonidas J. Guibas: colleagues