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Least squares conformal maps for automatic texture atlas generation
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Source ACM Transactions on Graphics (TOG) archive
Volume 21 ,  Issue 3  (July 2002) table of contents
Special issue: Proceedings of ACM SIGGRAPH 2002
SESSION: Parameterization and meshes table of contents
Pages: 362 - 371  
Year of Publication: 2002
ISSN:0730-0301
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Authors
Bruno Lévy  ISA (Inria Lorraine and CNRS), France
Sylvain Petitjean  ISA (Inria Lorraine and CNRS), France
Nicolas Ray  ISA (Inria Lorraine and CNRS), France
Jérome Maillot  ISA (Inria Lorraine and CNRS), France and Alias|Wavefront
Publisher
ACM  New York, NY, USA
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ABSTRACT

A Texture Atlas is an efficient color representation for 3D Paint Systems. The model to be textured is decomposed into charts homeomorphic to discs, each chart is parameterized, and the unfolded charts are packed in texture space. Existing texture atlas methods for triangulated surfaces suffer from several limitations, requiring them to generate a large number of small charts with simple borders. The discontinuities between the charts cause artifacts, and make it difficult to paint large areas with regular patterns.In this paper, our main contribution is a new quasi-conformal parameterization method, based on a least-squares approximation of the Cauchy-Riemann equations. The so-defined objective function minimizes angle deformations, and we prove the following properties: the minimum is unique, independent of a similarity in texture space, independent of the resolution of the mesh and cannot generate triangle flips. The function is numerically well behaved and can therefore be very efficiently minimized. Our approach is robust, and can parameterize large charts with complex borders.We also introduce segmentation methods to decompose the model into charts with natural shapes, and a new packing algorithm to gather them in texture space. We demonstrate our approach applied to paint both scanned and modeled data sets.


REFERENCES

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CITED BY  98
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Collaborative Colleagues:
Bruno Lévy: colleagues
Sylvain Petitjean: colleagues
Nicolas Ray: colleagues
Jérome Maillot: colleagues