D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry

@article{Yang2020D3VODD,
  title={D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry},
  author={Nan Yang and L. Stumberg and Rui Wang and D. Cremers},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={1278-1289}
}
  • Nan Yang, L. Stumberg, +1 author D. Cremers
  • Published 2020
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. In particular, it aligns the training image pairs into similar lighting condition with predictive brightness transformation parameters. Besides, we model the photometric uncertainties of pixels on the… Expand
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