de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Mesh Segmentation Driven by Gaussian Curvature

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45769

Yamauchi,  Hitoshi
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44544

Gumhold,  Stefan
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45789

Zayer,  Rhaleb
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Yamauchi, H., Gumhold, S., Zayer, R., & Seidel, H.-P. (2005). Mesh Segmentation Driven by Gaussian Curvature. The Visual Computer, 21, 649-658.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-270A-F
Zusammenfassung
Mesh parameterization is a fundamental problem in computer graphics as it allows for texture mapping and facilitates a lot of mesh processing tasks. Although there exists a variety of good parameterization methods for meshes that are topologically equivalent to a disc, the segmentation into nicely parameterizable charts of higher genus meshes has been studied less. In this paper we propose a new segmentation method for the generation of charts that can be flattened efficiently. The integrated Gaussian curvature is used to measure the developability of a chart and a robust and simple scheme is proposed to integrate the Gaussian curvature. The segmentation approach evenly distributes Gaussian curvature over the charts and automatically ensures disc-like topology of each chart. For numerical stability, we use area on the Gauss map to represent Gaussian curvature. Resulting parameterization shows that charts generated in this way have less distortion compared to charts generated by other methods.