SSD-6D: Making RGB-Based 3D Detection and 6d Pose Estimation Great Again

Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, Nassir Navab

ICCV 2017

doi:10.1109/ICCV.2017.169 /iccv/2017/kehl2017iccv-ssd6d/

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 RGB-D 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.

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Cite

Text

Kehl et al. "SSD-6D: Making RGB-Based 3D Detection and 6d Pose Estimation Great Again." International Conference on Computer Vision, 2017. doi:10.1109/ICCV.2017.169

Markdown

[Kehl et al. "SSD-6D: Making RGB-Based 3D Detection and 6d Pose Estimation Great Again." International Conference on Computer Vision, 2017.](https://mlanthology.org/iccv/2017/kehl2017iccv-ssd6d/) doi:10.1109/ICCV.2017.169

BibTeX

@inproceedings{kehl2017iccv-ssd6d,
  title     = {{SSD-6D: Making RGB-Based 3D Detection and 6d Pose Estimation Great Again}},
  author    = {Kehl, Wadim and Manhardt, Fabian and Tombari, Federico and Ilic, Slobodan and Navab, Nassir},
  booktitle = {International Conference on Computer Vision},
  year      = {2017},
  doi       = {10.1109/ICCV.2017.169},
  url       = {https://mlanthology.org/iccv/2017/kehl2017iccv-ssd6d/}
}