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.
@article{arxiv.1711.10006,
title = {SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again},
author = {Wadim Kehl and Fabian Manhardt and Federico Tombari and Slobodan Ilic and Nassir Navab},
journal= {arXiv preprint arXiv:1711.10006},
year = {2017}
}
Comments
The first two authors contributed equally to this work