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 ABSTRACT
This paper presents a method of constructing a smooth function of two or more variables that interpolates data values at arbitrarily distributed points. Shepard's method for fitting a surface to data values at scattered points in the plane has the advantages of a small storage requirement and an easy generalization to more than two independent variables, but suffers from low accuracy and a high computational cost relative to some alternative methods. Localizations of this method have reasonably low computational costs, but remain relatively inaccurate. We describe a modified Shepard's method that, without sacrificing the advantages, has accuracy comparable to other local methods. Computational efficiency is also improved by using a cell method for nearest-neighbor searching. Test results for two and three independent variables are presented.
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 28
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Vlastimil Havran , Attila Neumann , Georg Zotti , Werner Purgathofer , Hans-Peter Seidel, On cross-validation and resampling of BRDF data measurements, Proceedings of the 21st spring conference on Computer graphics, May 12-14, 2005, Budmerice, Slovakia
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Yutaka Ohtake , Alexander Belyaev , Marc Alexa , Greg Turk , Hans-Peter Seidel, Multi-level partition of unity implicits, ACM SIGGRAPH 2005 Courses, July 31-August 04, 2005, Los Angeles, California
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REVIEW
"Alan Charles Genz : Reviewer"
This paper describes an algorithm for the multivariate interpolation of
data that may be sparse and unstructured. The main difficulty with this
type of problem is the quick determination of a representation of data
that is accurate and can be ef
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