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

J. Lienen, E. Hüllermeier, ArXiv:2010.13118 (2020).

Preprint | English
Abstract
In many real-world applications, the relative depth of objects in an image is crucial for scene understanding, e.g., to calculate occlusions in augmented reality scenes. Predicting depth in monocular images has recently been tackled using machine learning methods, mainly 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 camera than B") have shown promising performance on this problem. In this paper, we elaborate on the use of so-called \emph{listwise} ranking as a generalization of the pairwise approach. Listwise ranking goes beyond pairwise comparisons between objects and considers rankings of arbitrary length as training information. Our approach is based on the Plackett-Luce model, a probability distribution on rankings, which we combine with a state-of-the-art neural network architecture and a sampling strategy to reduce training complexity. An empirical evaluation on benchmark data in a "zero-shot" setting demonstrates the effectiveness of our proposal compared to existing ranking and regression methods.
Publishing Year
Journal Title
arXiv:2010.13118
LibreCat-ID

Cite this

Lienen J, Hüllermeier E. Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model. arXiv:201013118. 2020.
Lienen, J., & Hüllermeier, E. (2020). Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model. ArXiv:2010.13118.
@article{Lienen_Hüllermeier_2020, title={Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model}, journal={arXiv:2010.13118}, author={Lienen, Julian and Hüllermeier, Eyke}, year={2020} }
Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce  Model.” ArXiv:2010.13118, 2020.
J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model,” arXiv:2010.13118. 2020.
Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce  Model.” ArXiv:2010.13118, 2020.
All files available under the following license(s):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]

Link(s) to Main File(s)
Access Level
Restricted Closed Access

Export

Marked Publications

Open Data LibreCat

Search this title in

Google Scholar