SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
Kehl W, Manhardt F, Tombari F, Ilic S, Navab N (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2017-October
Pages Range: 1530-1538
Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision
Event location: Venice, ITA
ISBN: 9781538610329
Abstract
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only. Our approach competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets. Furthermore, our method produces these results at around 10Hz, which is many times faster than the related methods. For the sake of reproducibility, we make our trained networks and detection code publicly available.
Involved external institutions
United States (USA) (US)
Germany (DE)How to cite
APA:
Kehl, W., Manhardt, F., Tombari, F., Ilic, S., & Navab, N. (2017). SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1530-1538). Venice, ITA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Kehl, Wadim, et al. "SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again." Proceedings of the 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, ITA Institute of Electrical and Electronics Engineers Inc., 2017. 1530-1538.
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