Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model

@article{Lienen2021MonocularDE,
  title={Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model},
  author={Julian Lienen and Eyke Hullermeier},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={14590-14599}
}
  • Julian Lienen, E. Hullermeier
  • Published 25 October 2020
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
In many real-world applications, the relative depth of objects in an image is crucial for scene understanding. Recent approaches mainly tackle the problem of depth prediction in monocular images by treating the problem as a regression task. Yet, being interested in an order relation in the first place, ranking methods suggest themselves as a natural alternative to regression, and indeed, ranking approaches leveraging pairwise comparisons as training information ("object A is closer to the… 

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