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

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Joint 3D-reconstruction and Background Separation in Multiple Views using Graph Cuts

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44508

Goldluecke,  Bastian
International Max Planck Research School, MPI for Informatics, Max Planck Society;
Graphics - Optics - Vision, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44965

Magnor,  Marcus
Graphics - Optics - Vision, 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

Goldluecke, B., & Magnor, M. (2003). Joint 3D-reconstruction and Background Separation in Multiple Views using Graph Cuts. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR-03) (pp. 683-694). Los Alamitos, USA: IEEE.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-2D5A-F
Zusammenfassung
This paper deals with simultaneous depth map estimation and background separation in a multi-view setting with several fixed calibrated cameras, two problems which have previously been addressed separately. We demonstrate that their strong interdependency can be exploited elegantly by minimizing a discrete energy functional which evaluates both properties at the same time. Our algorithm is derived from the powerful ``Multi-Camera Scene Reconstruction via Graph Cuts'' algorithm recently presented by Kolmogorov and Zabih. Experiments with both real-world as well as synthetic scenes demonstrate that the presented combined approach yields even more correct depth estimates. In particular, the additional information gained by taking background into account increases considerably the algorithm's robustness against noise.