DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets

Yongjie Guan, Xueyu Hou, Nan Wu, Bo Han, Tao Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Mobile headsets should be capable of understanding 3D physical environments to offer a truly immersive experience for augmented/mixed reality (AR/MR). However, their small form-factor and limited computation resources make it extremely challenging to execute in real-time 3D vision algorithms, which are known to be more compute-intensive than their 2D counterparts. In this paper, we propose DeepMix, a mobility-aware, lightweight, and hybrid 3D object detection framework for improving the user experience of AR/MR on mobile headsets. Motivated by our analysis and evaluation of state-of-the-art 3D object detection models, DeepMix intelligently combines edge-assisted 2D object detection and novel, on-device 3D bounding box estimations that leverage depth data captured by headsets. This leads to low end-to-end latency and significantly boosts detection accuracy in mobile scenarios. A unique feature of DeepMix is that it fully exploits the mobility of headsets to fine-tune detection results and boost detection accuracy. To the best of our knowledge, DeepMix is the first 3D object detection that achieves 30 FPS (i.e., an end-to-end latency much lower than the 100 ms stringent requirement of interactive AR/MR). We implement a prototype of DeepMix on Microsoft HoloLens and evaluate its performance via both extensive controlled experiments and a user study with 30+ participants. DeepMix not only improves detection accuracy by 9.1 - 37.3% but also reduces end-to-end latency by 2.68 - 9.15×, compared to the baseline that uses existing 3D object detection models.

Original languageEnglish (US)
Title of host publicationMobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services
PublisherAssociation for Computing Machinery, Inc
Pages28-41
Number of pages14
ISBN (Electronic)9781450391856
DOIs
StatePublished - Jun 27 2022
Event20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022 - Portland, United States
Duration: Jun 27 2022Jul 1 2022

Publication series

NameMobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services

Conference

Conference20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022
Country/TerritoryUnited States
CityPortland
Period6/27/227/1/22

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • 3D object detection
  • augmented and mixed reality
  • hybrid mobile vision
  • mobile headsets

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