Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes
Hinterstoisser S, Lepetit V, Ilic S, Holzer S, Bradski G, Konolige K, Navab N (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 7724 LNCS
Pages Range: 548-562
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: KOR
ISBN: 9783642373305
DOI: 10.1007/978-3-642-37331-2_42
Abstract
We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach [1] for object detection. We show how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time. The pose estimation and the color information allow us to check the detection hypotheses and improves the correct detection rate by 13% with respect to the original LINEMOD. These many improvements make our framework suitable for object manipulation in Robotics applications. Moreover we propose a new dataset made of 15 registered, 1100+ frame video sequences of 15 various objects for the evaluation of future competing methods. © 2013 Springer-Verlag.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
École Polytechnique Fédérale de Lausanne (EPFL)
Switzerland (CH)
Germany (DE)
École Polytechnique Fédérale de Lausanne (EPFL)
Switzerland (CH)
How to cite
APA:
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., & Navab, N. (2013). Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 548-562). KOR.
MLA:
Hinterstoisser, Stefan, et al. "Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes." Proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012, KOR 2013. 548-562.
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