Monocular Depth Estimation: a Review of the 2022 State of the Art
Thibaud Ehret
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. **This is an MLBriefs article, the source code has not been reviewed!**<br> <span style="color:red">**BEST PAPER MLBRIEFS 2022**</span>. <br> <br> The codes used in the demos and publicly available are the following: * [[MiDaS method|MiDaS-main.zip]], * [[DPT method|DPT-master.zip]], * [[Adabins method|Adabins-main.zip]], * [[GLPDepth method|GLPDepth-main.zip]], and the original source codes are available here (last checked 2023/01/23): * [[MiDaS and DPT methods|https://github.com/isl-org/MiDaS]], * [[Adabins method|https://github.com/shariqfarooq123/AdaBins]], * [[GLPDepth method|https://github.com/vinvino02/GLPDepth]], * [[3DShape method|https://github.com/aim-uofa/AdelaiDepth]]