| Piecewise smooth surface reconstruction |
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International Conference on Computer Graphics and Interactive Techniques
archive
Proceedings of the 21st annual conference on Computer graphics and interactive techniques
table of contents
Pages: 295 - 302
Year of Publication: 1994
ISBN:0-89791-667-0
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Authors
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Hugues Hoppe
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University of Washington, Seattle, WA and Department of Computer Science and Engineering, FR-35
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Tony DeRose
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University of Washington, Seattle, WA and Department of Computer Science and Engineering, FR-35
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Tom Duchamp
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University of Washington, Seattle, WA and Department of Mathematics, GN-50
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Mark Halstead
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University of Washington, Seattle, WA and Apple Computer
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Hubert Jin
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University of Washington, Seattle, WA and Department of Statistics, GN-22
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John McDonald
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University of Washington, Seattle, WA and Department of Statistics, GN-22
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Jean Schweitzer
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University of Washington, Seattle, WA and Department of Computer Science and Engineering, FR-35
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Werner Stuetzle
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University of Washington, Seattle, WA and Department of Statistics, GN-22
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| Sponsor |
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| Bibliometrics |
Downloads (6 Weeks): 29, Downloads (12 Months): 132, Citation Count: 110
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ABSTRACT
We present a general method for automatic reconstruction of accurate, concise, piecewise smooth surface models from scattered range data. The method can be used in a variety of applications such as reverse engineering—the automatic generation of CAD models from physical objects. Novel aspects of the method are its ability to model surfaces of arbitrary topological type and to recover sharp features such as creases and corners. The method has proven to be effective, as demonstrated by a number of examples using both simulated and real data.A key ingredient in the method, and a principal contribution of this paper, is the introduction of a new class of piecewise smooth surface representations based on subdivision. These surfaces have a number of properties that make them ideal for use in surface reconstruction: they are simple to implement, they can model sharp features concisely, and they can be fit to scattered range data using an unconstrained optimization procedure.
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 110
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