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Cited by 308

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction

Innmann, Matthias, Zollhöfer, Michael, Nießner, Matthias, Theobalt, Christian, Stamminger, Marc·2016·Lecture Notes in Computer Science

Abstract

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 builds up the scene model from scratch during the scanning process, thus it does not require a pre-defined shape template to start with. 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 constraint. 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.

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Metadata

Authors
Innmann, M., Zollhöfer, M., Nießner, M., Theobalt, C., & Stamminger, M.
Venue
Lecture Notes in Computer Science
Published
2016
Pages
362-379
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2325034626
Cited by
308
References
59
FWCI
77.46
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction. https://app.cochat.ai/papers/10.1007/978-3-319-46484-8_22
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Innmann, M., Zollhöfer, M., Nießner, M., Theobalt, C., & Stamminger, M. (2016). VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-46484-8_22

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.