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Cited by 259

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Texture mapping using surface flattening via multidimensional scaling

Zigelman, G., Kimmel, R., Kiryati, N.·2002·IEEE Transactions on Visualization and Computer Graphics

Abstract

Presents a novel technique for texture mapping on arbitrary surfaces with minimal distortion by preserving the local and global structure of the texture. The recent introduction of the fast marching method on triangulated surfaces has made it possible to compute a geodesic distance map from a given surface point in O(n lg n) operations, where n is the number of triangles that represent the surface. We use this method to design a surface flattening approach based on multi-dimensional scaling (MDS). MDS is a family of methods that map a set of points into a finite-dimensional flat (Euclidean) domain, where the only data given is the corresponding distance between every pair of points. The MDS mapping yields minimal changes of the distances between the corresponding points. We then solve an "inverse" problem and map a flat texture patch onto a curved surface while preserving the structure of the texture.

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Metadata

Authors
Zigelman, G., Kimmel, R., & Kiryati, N.
Venue
IEEE Transactions on Visualization and Computer Graphics
Published
2002
Volume
8
Issue
2
Pages
198-207
ISSN
1077-2626 (ISSN-L)
OpenAlex
W2134921160
Cited by
259
References
1
FWCI
34.85
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Texture mapping using surface flattening via multidimensional scaling. https://app.cochat.ai/papers/10.1109/2945.998671
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Zigelman, G., Kimmel, R., & Kiryati, N. (2002). Texture mapping using surface flattening via multidimensional scaling. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/2945.998671

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.