Outside-in monocular IR camera based HMD pose estimation via geometric optimization

Pavel A. Savkin, Shunsuke Saito, Jarich Vansteenberge, Tsukasa Fukusato, Lochlainn Wilson, Shigeo Morishima

    研究成果: Conference contribution

    抜粋

    Accurately tracking a Head Mounted Display (HMD) with a 6 degree of freedom is essential to achieve a comfortable and a nausea free experience in Virtual Reality. Existing commercial HMD systems using synchronized Infrared (IR) camera and blinking IR-LEDs can achieve highly accurate tracking. However, most of the o-the-shelf cameras do not support frame synchronization. In this paper, we propose a novel method for real time HMD pose estimation without using any camera synchronization or LED blinking. We extended over the state of the art pose estimation algorithm by introducing geometrically constrained optimization. In addition, we propose a novel system to increase robustness to the blurred IR-LEDs paerns appearing at high-velocity movements. The quantitative evaluations showed signicant improvements in pose stability and accuracy over wide rotational movements as well as a decrease in runtime.

    元の言語English
    ホスト出版物のタイトルProceedings - VRST 2017
    ホスト出版物のサブタイトル23rd ACM Conference on Virtual Reality Software and Technology
    出版者Association for Computing Machinery
    Part F131944
    ISBN(電子版)9781450355483
    DOI
    出版物ステータスPublished - 2017 11 8
    イベント23rd ACM Conference on Virtual Reality Software and Technology, VRST 2017 - Gothenburg, Sweden
    継続期間: 2017 11 82017 11 10

    Other

    Other23rd ACM Conference on Virtual Reality Software and Technology, VRST 2017
    Sweden
    Gothenburg
    期間17/11/817/11/10

    ASJC Scopus subject areas

    • Software

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  • これを引用

    Savkin, P. A., Saito, S., Vansteenberge, J., Fukusato, T., Wilson, L., & Morishima, S. (2017). Outside-in monocular IR camera based HMD pose estimation via geometric optimization. : Proceedings - VRST 2017: 23rd ACM Conference on Virtual Reality Software and Technology (巻 Part F131944). [a7] Association for Computing Machinery. https://doi.org/10.1145/3139131.3139136