Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

A Point-based Approach to PDE-based Surface Reconstruction

MPG-Autoren
/persons/resource/persons44929

Linz,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;
Graphics - Optics - Vision, MPI for Informatics, Max Planck Society;

/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;

/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

Linz, C., Goldluecke, B., & Magnor, M. (2006). A Point-based Approach to PDE-based Surface Reconstruction. In K. Franke, K. R. Müller, B. Nickolay, & R. Schäfer (Eds.), Pattern Recognition : 28th DAGM Symposium (DAGM'06) (pp. 729-738). Berlin, Germany: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-24A7-5
Zusammenfassung
Variational techniques are a popular approach for reconstructing the surface of an object. In previous work, the surface is represented either implicitly by the use of level sets or explicitly as a triangle mesh. In this paper we describe new formulations and develop fast algorithms for surface reconstruction based on partial differential equations (PDEs) derived from variational calculus using an explicit, purely point-based surface representation. The method is based on a Moving Least-Squares surface approximation of the sample points. Our new approach automatically copes with complicated topology and deformations, without the need for explicit treatment. In contrast to level sets, it requires no postprocessing, easily adapts to varying spatial resolutions and is invariant under rigid body motion. We demonstrate the versatility of our method using several synthetic data sets and show how our technique can be used to reconstruct object surfaces from real-world multi-view footage.