Egocentric 6-DoF Tracking of Small Handheld Objects release_77fv6el3fvgdnlct5umyzm6jke

by Rohit Pandey, Pavel Pidlypenskyi, Shuoran Yang, Christine Kaeser-Chen

Released as a article .

2018  

Abstract

Virtual and augmented reality technologies have seen significant growth in the past few years. A key component of such systems is the ability to track the pose of head mounted displays and controllers in 3D space. We tackle the problem of efficient 6-DoF tracking of a handheld controller from egocentric camera perspectives. We collected the HMD Controller dataset which consist of over 540,000 stereo image pairs labelled with the full 6-DoF pose of the handheld controller. Our proposed SSD-AF-Stereo3D model achieves a mean average error of 33.5 millimeters in 3D keypoint prediction and is used in conjunction with an IMU sensor on the controller to enable 6-DoF tracking. We also present results on approaches for model based full 6-DoF tracking. All our models operate under the strict constraints of real time mobile CPU inference.
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Type  article
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Date   2018-04-16
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arXiv  1804.05870v1
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