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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 run-time 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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PublisherIEEE Computer Society
Pages2545-2557
Number of pages13
ISBN (Electronic)9781665448994
DOIs
StatePublished - Jun 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online
Duration: 19 Jun 202125 Jun 2021

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
CityVirtual, Online
Period19/06/2125/06/21

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