[IPOL announce] new article: Monocular Depth Estimation: a Review of the 2022 State of the Art

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2023/459/

Thibaud Ehret,
Monocular Depth Estimation: a Review of the 2022 State of the Art,
Image Processing On Line, 13 (2023), pp. 38–56.
https://doi.org/10.5201/ipol.2023.459

Abstract
We compare five monocular depth estimation methods based on deep 
learning. This comparison focuses on how well methods generalize rather 
than a quantitative comparison on a specific dataset. This study shows 
that while monocular depth estimation methods work well on images 
similar to training images, they often show artifacts when applied on 
images out of the training distribution. We evaluate the different 
methods with images similar to training data and images with unusual 
point of views (e.g. top-down) or paintings. The readers are invited to 
judge by themselves about the advantages and drawbacks of all methods by 
submitting their own images to the online demo associated with the 
present paper.