Publikation: An Efficient Octree Design for Local Variational Range Image Fusion
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2017
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European Union (EU): 336978
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LIA - Light Field Imaging and Analysis
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ROTH, Volker, ed., Thomas VETTER, ed.. Pattern Recognition. Cham: Springer, 2017, pp. 401-412. Lecture Notes in Computer Science. 10496. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-66708-9. Available under: doi: 10.1007/978-3-319-66709-6_32
Zusammenfassung
We present a reconstruction pipeline for a large-scale 3D environment viewed by a single moving RGB-D camera. Our approach combines advantages of fast and direct, regularization-free depth fusion and accurate, but costly variational schemes. The scene’s depth geometry is extracted from each camera view and efficiently integrated into a large, dense grid as a truncated signed distance function, which is organized in an octree. To account for noisy real-world input data, variational range image integration is performed in local regions of the volume directly on this octree structure. We focus on algorithms which are easily parallelizable on GPUs, allowing the pipeline to be used in real-time scenarios where the user can interactively view the reconstruction and adapt camera motion as required.
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004 Informatik
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39th German Conference : GCPR 2017 Proceedings, 12. Sept. 2017 - 15. Sept. 2017, Basel, Switzerland
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MARNIOK, Nico, Ole JOHANNSEN, Bastian GOLDLÜCKE, 2017. An Efficient Octree Design for Local Variational Range Image Fusion. 39th German Conference : GCPR 2017 Proceedings. Basel, Switzerland, 12. Sept. 2017 - 15. Sept. 2017. In: ROTH, Volker, ed., Thomas VETTER, ed.. Pattern Recognition. Cham: Springer, 2017, pp. 401-412. Lecture Notes in Computer Science. 10496. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-66708-9. Available under: doi: 10.1007/978-3-319-66709-6_32BibTex
@inproceedings{Marniok2017Effic-42780,
year={2017},
doi={10.1007/978-3-319-66709-6_32},
title={An Efficient Octree Design for Local Variational Range Image Fusion},
number={10496},
isbn={978-3-319-66708-9},
issn={0302-9743},
publisher={Springer},
address={Cham},
series={Lecture Notes in Computer Science},
booktitle={Pattern Recognition},
pages={401--412},
editor={Roth, Volker and Vetter, Thomas},
author={Marniok, Nico and Johannsen, Ole and Goldlücke, Bastian}
}RDF
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<dcterms:abstract xml:lang="eng">We present a reconstruction pipeline for a large-scale 3D environment viewed by a single moving RGB-D camera. Our approach combines advantages of fast and direct, regularization-free depth fusion and accurate, but costly variational schemes. The scene’s depth geometry is extracted from each camera view and efficiently integrated into a large, dense grid as a truncated signed distance function, which is organized in an octree. To account for noisy real-world input data, variational range image integration is performed in local regions of the volume directly on this octree structure. We focus on algorithms which are easily parallelizable on GPUs, allowing the pipeline to be used in real-time scenarios where the user can interactively view the reconstruction and adapt camera motion as required.</dcterms:abstract>
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