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3D Reconstruction of Reflection Nebulae from a Single Image

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44928

Lintu,  Andrei
Computer Graphics, 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;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44911

Lensch,  Hendrik P. A.
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;

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Zitation

Lintu, A., Hoffmann, L., Magnor, M., Lensch, H. P. A., & Seidel, H.-P. (2007). 3D Reconstruction of Reflection Nebulae from a Single Image. In H. P. A. Lensch, B. Rosenhahn, H.-P. Seidel, P. Slusallek, & J. Weickert (Eds.), Vision, Modeling, and Visualization 2007: proceedings (pp. 109-116). Saarbrücken, Germany: Max-Planck-Institut für Informatik.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1DCE-C
Zusammenfassung
This paper presents a method for reconstructing the 3D distribution of dust densities in reflection nebulae based on a single input image using an analysisby- synthesis approach. In a reflection nebula, light is typically emitted from a central star and then scattered and partially absorbed by the nebula’s dust particles. We model the light transport in this kind of nebulae by considering absorption and single scattering only. While the core problem of reconstructing an arbitrary 3D volume of dust particles from a 2D image would be ill-posed we demonstrate how the special configuration of light transport paths in reflection nebulae allows us to produce non-exact but plausible 3D volumes. Our reconstruction is driven by an iterative non-linear optimization method, which renders an image in each step with the current estimate of dust densities and then updates the density values to minimize the error to the input image. The recovered volumetric datasets can be used in astrophysical research as well as planetarium visualizations.