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    METHOD AND APPARATUS FOR CALIBRATING AUGMENTED REALITY HEADSETS
    1.
    发明公开
    METHOD AND APPARATUS FOR CALIBRATING AUGMENTED REALITY HEADSETS 审中-公开

    公开(公告)号：US20230419460A1

    公开(公告)日：2023-12-28

    申请号：US18367947

    申请日：2023-09-13

    申请人： Ultrahaptics IP Two Limited

    发明人： Johnathon Scott SELSTAD ,  David Samuel HOLZ

    IPC分类号： G06T5/00 ,  G06T19/20 ,  G06T19/00

    CPC分类号： G06T5/006 ,  G06T19/20 ,  G06T19/006

    摘要： An AR calibration system for correcting AR headset distortions. A calibration image is provided to a screen and viewable through a headset reflector, and an inverse of the calibration image is provided to a headset display, reflected off the reflector and observed by a camera of the system while it is simultaneously observing the calibration image on the screen. One or more cameras are located to represent a user's point of view and aligned to observe the inverse calibration image projected onto the reflector. A distortion mapping transform is created using an algorithm to search through projection positions of the inverse calibration image until the inverse image observed by the camera(s) cancels out an acceptable portion of the calibration image provided to the screen as observed through the reflector by the camera, and the transform is used by the headset, to compensate for distortions.

    Hand Pose Estimation for Machine Learning Based Gesture Recognition
    2.
    发明公开
    Hand Pose Estimation for Machine Learning Based Gesture Recognition 审中-公开

    公开(公告)号：US20230214458A1

    公开(公告)日：2023-07-06

    申请号：US16508231

    申请日：2019-07-10

    申请人： Ultrahaptics IP Two Limited

    发明人： Jonathan MARSDEN ,  Raffi BEDIKIAN ,  David Samuel HOLZ

    IPC分类号： G06K9/62 ,  G06K9/00 ,  G06K9/46 ,  G06T7/13

    CPC分类号： G06K9/6269 ,  G06K9/00355 ,  G06K9/4604 ,  G06T7/13 ,  G06T2207/10028

    摘要： The technology disclosed performs hand pose estimation on a so-called “joint-by-joint” basis. So, when a plurality of estimates for the 28 hand joints are received from a plurality of expert networks (and from master experts in some high-confidence scenarios), the estimates are analyzed at a joint level and a final location for each joint is calculated based on the plurality of estimates for a particular joint. This is a novel solution discovered by the technology disclosed because nothing in the field of art determines hand pose estimates at such granularity and precision. Regarding granularity and precision, because hand pose estimates are computed on a joint-by joint basis, this allows the technology disclosed to detect in real time even the minutest and most subtle hand movements, such a bend/yaw/tilt/roll of a segment of a finger or a tilt an occluded finger, as demonstrated supra in the Experimental Results section of this application.