Comparing Monocular Camera Depth Estimation Models for Real-time Applications

Abdelrahman Diab, Mohamed Sabry, Amr El Mougy

2022

Abstract

Monocular Depth Estimation (MDE) is a fundamental problem in the field of Computer Vision with ongoing developments. For the case of challenging applications such as autonomous driving, where highly accurate results are required in real-time, traditional approaches fall short due to insufficient information to understand the scene geometry. Novel approaches utilizing deep neural networks show significantly improved results, especially in autonomous driving applications. Nevertheless, there now exists a number of promising approaches in literature and their performance has never been compared head-to-head. In this paper, a detailed evaluation of the performance of four selected deep learning networks is presented. We identify a set of metrics to benchmark the selected approaches from different aspects, especially those related to real-time applications. We analyze the results and present insights into the performance levels of the various approaches.

Paper Citation

in Harvard Style

Diab A., Sabry M. and El Mougy A. (2022). Comparing Monocular Camera Depth Estimation Models for Real-time Applications. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 673-680. DOI: 10.5220/0010883700003116

in Bibtex Style

@conference{icaart22,
author={Abdelrahman Diab and Mohamed Sabry and Amr El Mougy},
title={Comparing Monocular Camera Depth Estimation Models for Real-time Applications},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={673-680},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010883700003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Comparing Monocular Camera Depth Estimation Models for Real-time Applications
SN - 978-989-758-547-0
AU - Diab A.
AU - Sabry M.
AU - El Mougy A.
PY - 2022
SP - 673
EP - 680
DO - 10.5220/0010883700003116