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VolumeDeform: Real-time Volumetric Non-rigid Reconstruction

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons136490

Zollhöfer,  Michael
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45610

Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Volltexte (frei zugänglich)

arXiv:1603.08161.pdf
(Preprint), 3MB

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Zitation

Innmann, M., Zollhöfer, M., Nießner, M., Theobalt, C., & Stamminger, M. (2016). VolumeDeform: Real-time Volumetric Non-rigid Reconstruction. Retrieved from http://arxiv.org/abs/1603.08161.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002B-9A8E-6
Zusammenfassung
We present a novel approach for the reconstruction of dynamic geometric shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates. Our method does not require a pre-defined shape template to start with and builds up the scene model from scratch during the scanning process. Geometry and motion are parameterized in a unified manner by a volumetric representation that encodes a distance field of the surface geometry as well as the non-rigid space deformation. Motion tracking is based on a set of extracted sparse color features in combination with a dense depth-based constraint formulation. This enables accurate tracking and drastically reduces drift inherent to standard model-to-depth alignment. We cast finding the optimal deformation of space as a non-linear regularized variational optimization problem by enforcing local smoothness and proximity to the input constraints. The problem is tackled in real-time at the camera's capture rate using a data-parallel flip-flop optimization strategy. Our results demonstrate robust tracking even for fast motion and scenes that lack geometric features.