Monocular Depth Estimation: a Review of the 2022 State of the Art
Thibaud Ehret
Thibaud Ehret, Monocular Depth Estimation: a Review of the 2022 State of the Art, Image Processing On Line, 13 (2023), pp. 38–56.

Communicated by Jean-Michel Morel
Demo edited by Thibaud Ehret


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.

This is an MLBriefs article, the source code has not been reviewed!


The codes used in the demos and publicly available are the following:

and the original source codes are available here (last checked 2023/01/23):