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Graphical Models
Article . 2002 . Peer-reviewed
License: Elsevier TDM
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Article . 2020
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Scattered Data Interpolation Using Data Dependant Optimization Techniques

Scattered data interpolation using data dependant optimization techniques.
Authors: Günther Greiner; Andreas Kolb 0001; Angela Riepl;

Scattered Data Interpolation Using Data Dependant Optimization Techniques

Abstract

Summary: Interpolation of scattered data has many applications in different areas. Recently, this problem has gained a lot of interest for CAD applications, in combination with the process of reverse engineering, i.e., the construction of CAD models for existing objects. Until now, no method for scattered data interpolation with a bivariate function has produced surface formats that can be directly integrated into a CAD system. Additionally many of the existing interpolation schemes exhibit undesirable curvature distribution of the reconstructed surface. In this paper we present a method for scattered data interpolation producing tensor--product B-splines with high quality curvature distribution. This method first determines the knot vectors in a way that guarantees the existence of an interpolating B-spline. In a second step the degrees of freedom not specified by the interpolation constraints are automatically set using a data dependent optimization technique. Examples demonstrate the quality of the resulting interpolants w.r.t. curvature distribution and approximation of known surfaces.

Related Organizations
Keywords

B-splines, Computer graphics; computational geometry (digital and algorithmic aspects), CAD system, Computer science aspects of computer-aided design

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Top 10%
Average
gold