On-Site Adaptation for Monocular Depth Estimation with a Static Camera
Huan Li (Bologna University),* Matteo Poggi (University of Bologna), Fabio Tosi (University of Bologna), Stefano Mattoccia (University of Bologna)
The 34th British Machine Vision Conference
Abstract
We introduce a novel technique for easing the deployment of an off-the-shelf monocular depth estimation network in unseen environments. Specifically, we target a very diffused setting with a fixed camera mounted higher over the ground to monitor an environment and highlight the limitations of state-of-the-art monocular networks deployed in such a setup. Purposely, we develop an on-site adaptation technique capable of 1) improving the accuracy of estimated depth maps in the presence of moving subjects, such as pedestrians, cars, and others; 2) refining the overall structure of the predicted depth map, to make it more consistent with the real 3D structure of the scene; 3) recovering absolute metric depth, usually lost by state-of-the-art solutions. Experiments on synthetic and real datasets confirm the effectiveness of our proposal.Citation
@inproceedings{Li_2023_BMVC,
author = {Huan Li and Matteo Poggi and Fabio Tosi and Stefano Mattoccia},
title = {On-Site Adaptation for Monocular Depth Estimation with a Static Camera},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://bmvc2022.mpi-inf.mpg.de/BMVC2023/0901.pdf}
}
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