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Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report

Authors :
Ignatov, Andrey
Malivenko, Grigory
Plowman, David
Shukla, Samarth
Timofte, Radu
Zhang, Ziyu
Wang, Yicheng
Huang, Zilong
Luo, Guozhong
Yu, Gang
Fu, Bin
Wang, Yiran
Li, Xingyi
Shi, Min
Xian, Ke
Cao, Zhiguo
Du, Jin-Hua
Wu, Pei-Lin
Ge, Chao
Yao, Jiaoyang
Tu, Fangwen
Li, Bo
Yoo, Jung Eun
Seo, Kwanggyoon
Xu, Jialei
Li, Zhenyu
Liu, Xianming
Jiang, Junjun
Chen, Wei-Chi
Joya, Shayan
Fan, Huanhuan
Kang, Zhaobing
Li, Ang
Feng, Tianpeng
Liu, Yang
Sheng, Chuannan
Yin, Jian
Benavide, Fausto T.
Publication Year :
2021

Abstract

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper.<br />Comment: Mobile AI 2021 Workshop and Challenges: https://ai-benchmark.com/workshops/mai/2021/. arXiv admin note: text overlap with arXiv:2105.07809

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2105.08630
Document Type :
Working Paper