Efficient 6-DoF Tracking of Handheld Objects from an Egocentric Viewpoint
Abstract
Virtual and augmented reality technologies have seen significant growth in the past few years. A key component of such systems is the ability to track the pose of head mounted displays and controllers in 3D space. We tackle the problem of efficient 6-DoF tracking of a handheld controller from egocentric camera perspectives. We collected the HMD Controller dataset which consist of over 540,000 stereo image pairs labelled with the full 6-DoF pose of the handheld controller. Our proposed SSD-AF-Stereo3D model achieves a mean average error of 33.5 millimeters in 3D keypoint prediction and is used in conjunction with an IMU sensor on the controller to enable 6-DoF tracking. We also present results on approaches for model based full 6-DoF tracking. All our models operate under the strict constraints of real time mobile CPU inference.
Cite
Text
Pandey et al. "Efficient 6-DoF Tracking of Handheld Objects from an Egocentric Viewpoint." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01216-8_26Markdown
[Pandey et al. "Efficient 6-DoF Tracking of Handheld Objects from an Egocentric Viewpoint." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/pandey2018eccv-efficient/) doi:10.1007/978-3-030-01216-8_26BibTeX
@inproceedings{pandey2018eccv-efficient,
title = {{Efficient 6-DoF Tracking of Handheld Objects from an Egocentric Viewpoint}},
author = {Pandey, Rohit and Pidlypenskyi, Pavel and Yang, Shuoran and Kaeser-Chen, Christine},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01216-8_26},
url = {https://mlanthology.org/eccv/2018/pandey2018eccv-efficient/}
}