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Fight Ill-Posedness with Ill-Posedness: Single-Shot Variational Depth Super-Resolution from Shading

Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers
CVPR 2018
doi:10.1109/CVPR.2018.00025 /cvpr/2018/haefner2018cvpr-fight/

Abstract

We put forward a principled variational approach for up-sampling a single depth map to the resolution of the companion color image provided by an RGB-D sensor. We combine heterogeneous depth and color data in order to jointly solve the ill-posed depth super-resolution and shape-from-shading problems. The low-frequency geometric information necessary to disambiguate shape-from-shading is extracted from the low-resolution depth measurements and, symmetrically, the high-resolution photometric clues in the RGB image provide the high-frequency information required to disambiguate depth super-resolution.

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Text

Haefner et al. "Fight Ill-Posedness with Ill-Posedness: Single-Shot Variational Depth Super-Resolution from Shading." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00025

Markdown

[Haefner et al. "Fight Ill-Posedness with Ill-Posedness: Single-Shot Variational Depth Super-Resolution from Shading." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/haefner2018cvpr-fight/) doi:10.1109/CVPR.2018.00025

BibTeX

@inproceedings{haefner2018cvpr-fight,
  title     = {{Fight Ill-Posedness with Ill-Posedness: Single-Shot Variational Depth Super-Resolution from Shading}},
  author    = {Haefner, Bjoern and Quéau, Yvain and Möllenhoff, Thomas and Cremers, Daniel},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00025},
  url       = {https://mlanthology.org/cvpr/2018/haefner2018cvpr-fight/}
}