Random-forest-based initializer for solving inverse problem in 3D motion tracking systems

Ryo Sugawara, Jiawei Huang, Kazuki Takashima, Taku Komura, Yoshifumi Kitarmura

研究成果: Conference contribution

抄録

Many motion tracking systems require solving inverse problem to compute the tracking result from original sensor measurements. For real-time motion tracking, such typical solutions as the Gauss-Newton method for solving their inverse problems need an initial value to optimize the cost function through iterations. A powerful initializer is crucial to generate a proper initial value for every time instance and, for achieving continuous accurate tracking without errors and rapid tracking recovery even when it is temporally interrupted. An improper initial value easily causes optimization divergence, and cannot always lead to reasonable solutions. Therefore, we propose a new initializer based on random-forest to obtain proper initial values for efficient real-time inverse problem computation. Our method trains a random-forest model with varied massive inputs and corresponding outputs and uses it as an initializer for runtime optimization. As an instance, we apply our initializer to IM3D[1], which is a real-time magnetic 3D motion tracking system with multiple tiny, identifiable, wireless, occlusion-free passive markers (LC coils).

本文言語English
ホスト出版物のタイトルProceedings - VRST 2018
ホスト出版物のサブタイトル24th ACM Symposium on Virtual Reality Software and Technology
編集者Stephen N. Spencer
出版社Association for Computing Machinery
ISBN(電子版)9781450360869
DOI
出版ステータスPublished - 2018 11 28
イベント24th ACM Symposium on Virtual Reality Software and Technology, VRST 2018 - Tokyo, Japan
継続期間: 2018 11 282018 12 1

出版物シリーズ

名前Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST

Other

Other24th ACM Symposium on Virtual Reality Software and Technology, VRST 2018
CountryJapan
CityTokyo
Period18/11/2818/12/1

ASJC Scopus subject areas

  • Software

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