SDF-2-SDF: Highly accurate 3D object reconstruction
Slavcheva M, Kehl W, Navab N, Ilic S (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9905 LNCS
Pages Range: 680-696
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Amsterdam, NLD
ISBN: 9783319464473
DOI: 10.1007/978-3-319-46448-0_41
Abstract
This paper addresses the problem of 3D object reconstruction using RGB-D sensors.Our main contribution is a novel implicit-to-implicit surface registration scheme between signed distance fields (SDFs), utilized both for the real-time frame-to-frame camera tracking and for the subsequent global optimization. SDF-2-SDF registration circumvents expensive correspondence search and allows for incorporation of multiple geometric constraints without any dependence on texture, yielding highly accurate 3Dmodels.An extensive quantitative evaluation on real and synthetic data demonstrates improved tracking and higher fidelity reconstructions than a variety of state-of-the-art methods. We make our data publicly available, creating the first object reconstruction dataset to include ground-truth CAD models and RGB-D sequences from sensors of various quality.
Involved external institutions
Germany (DE)How to cite
APA:
Slavcheva, M., Kehl, W., Navab, N., & Ilic, S. (2016). SDF-2-SDF: Highly accurate 3D object reconstruction. In Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 680-696). Amsterdam, NLD: Springer Verlag.
MLA:
Slavcheva, Miroslava, et al. "SDF-2-SDF: Highly accurate 3D object reconstruction." Proceedings of the 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, NLD Ed. Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling, Springer Verlag, 2016. 680-696.
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