AAAI 2023technical1 citations
FC-TrackNet: Fast Convergence Net for 6D Pose Tracking in Synthetic Domains
Di Jia, Qian Wang, Jun Cao, Peng Cai, Zhiyang Jin
Abstract
In this work, we propose a fast convergence track net, or FC-TrackNet, based on a synthetic data-driven approach to maintaining long-term 6D pose tracking. Comparison experiments are performed on two different datasets, The results demonstrate that our approach can achieve a consistent tracking frequency of 90.9 Hz as well as higher accuracy than the state-of-the art approaches.
BibTeX
@article{Jia_Wang_Cao_Cai_Jin_2024, title={FC-TrackNet: Fast Convergence Net for 6D Pose Tracking in Synthetic Domains}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27077}, DOI={10.1609/aaai.v37i13.27077}, abstractNote={In this work, we propose a fast convergence track net, or FC-TrackNet, based on a synthetic data-driven approach to maintaining long-term 6D pose tracking. Comparison experiments are performed on two different datasets, The results demonstrate that our approach can achieve a consistent tracking frequency of 90.9 Hz as well as higher accuracy than the state-of-the art approaches.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Jia, Di and Wang, Qian and Cao, Jun and Cai, Peng and Jin, Zhiyang}, year={2024}, month={Jul.}, pages={16455-16457} }