Corpus ID: 226964886

Fast Uncertainty Quantification for Deep Object Pose Estimation

@article{Shi2020FastUQ,
  title={Fast Uncertainty Quantification for Deep Object Pose Estimation},
  author={G. Shi and Yifeng Zhu and J. Tremblay and Stan Birchfield and F. Ramos and A. Anandkumar and Yuke Zhu},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.07748}
}

G. Shi, Yifeng Zhu, +4 authors Yuke Zhu

Published 2020

Computer Science

ArXiv

Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transfer. Efficient and robust uncertainty quantification (UQ) in pose estimators is critically needed in many robotic tasks. In this work, we propose a simple, efficient, and plug-and-play UQ method for 6-DoF object pose estimation. We ensemble 2-3 pre-trained models with different neural network architectures and/or… CONTINUE READING

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed