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    Method and apparatus for calibrating augmented reality headsets
    1.
    发明授权
    Method and apparatus for calibrating augmented reality headsets 有权

    公开(公告)号：US12169918B2

    公开(公告)日：2024-12-17

    申请号：US18367947

    申请日：2023-09-13

    Applicant: Ultrahaptics IP Two Limited

    Inventor： Johnathon Scott Selstad ,  David Samuel Holz

    IPC: G06T5/80 ,  G06T19/00 ,  G06T19/20

    Abstract: 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.

    METHOD AND APPARATUS FOR CALIBRATING AUGMENTED REALITY HEADSETS
    2.
    发明申请
    METHOD AND APPARATUS FOR CALIBRATING AUGMENTED REALITY HEADSETS 有权

    公开(公告)号：US20220270218A1

    公开(公告)日：2022-08-25

    申请号：US17741096

    申请日：2022-05-10

    Applicant: Ultrahaptics IP Two Limited

    Inventor： Johnathon Scott SELSTAD ,  David Samuel Holz

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

    Abstract: An AR calibration system for correcting AR headset distortions. A calibration image is provided to an external 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 external 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 external screen as observed through the reflector by the camera, and the transform is used by the headset, to compensate for distortions.

    Method and apparatus for correcting geometric and optical aberrations in augmented reality
    3.
    发明授权
    Method and apparatus for correcting geometric and optical aberrations in augmented reality 有权

    公开(公告)号：US11354787B2

    公开(公告)日：2022-06-07

    申请号：US16675071

    申请日：2019-11-05

    Applicant: Ultrahaptics IP Two Limited

    Inventor： Johnathon Scott Selstad ,  David Samuel Holz

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

    Abstract: The disclosed technology teaches an AR calibration system for compensating for AR headset distortions. A calibration image is provided to an external 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 external screen. The camera is 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 cancels out an acceptable portion of the calibration image provided to the external 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
    4.
    发明授权
    Hand pose estimation for machine learning based gesture recognition 有权

    公开(公告)号：US12147505B1

    公开(公告)日：2024-11-19

    申请号：US18224373

    申请日：2023-07-20

    Applicant: ULTRAHAPTICS IP TWO LIMITED

    Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

    IPC: G06K9/62 ,  G06F18/2411 ,  G06K9/00 ,  G06T7/13 ,  G06V10/44 ,  G06V40/20

    Abstract: 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.

    Hand initialization for machine learning based gesture recognition
    6.
    发明授权
    Hand initialization for machine learning based gesture recognition 有权

    公开(公告)号：US12260679B1

    公开(公告)日：2025-03-25

    申请号：US18391574

    申请日：2023-12-20

    Applicant: ULTRAHAPTICS IP TWO LIMITED

    Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

    IPC: G06V40/20 ,  G06F3/01 ,  G06F18/21 ,  G06F18/22 ,  G06V10/44 ,  G06V30/194 ,  G06V40/10

    Abstract: The technology disclosed also initializes a new hand that enters the field of view of a gesture recognition system using a parallax detection module. The parallax detection module determines candidate regions of interest (ROI) for a given input hand image and computes depth, rotation and position information for the candidate ROI. Then, for each of the candidate ROI, an ImagePatch, which includes the hand, is extracted from the original input hand image to minimize processing of low-information pixels. Further, a hand classifier neural network is used to determine which ImagePatch most resembles a hand. For the qualified, most-hand like ImagePatch, a 3D virtual hand is initialized with depth, rotation and position matching that of the qualified ImagePatch.

    Machine learning based gesture recognition
    7.
    发明授权
    Machine learning based gesture recognition 有权

    公开(公告)号：US12229217B1

    公开(公告)日：2025-02-18

    申请号：US18536151

    申请日：2023-12-11

    Applicant: ULTRAHAPTICS IP TWO LIMITED

    Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

    IPC: G06F18/214 ,  G06F3/01 ,  G06F18/24 ,  G06N3/04 ,  G06N3/08 ,  G06T7/246 ,  G06T7/285 ,  G06T7/73 ,  G06V10/70 ,  G06V20/64 ,  G06V40/20

    Abstract: The technology disclosed introduces two types of neural networks: “master” or “generalists” networks and “expert” or “specialists” networks. Both, master networks and expert networks, are fully connected neural networks that take a feature vector of an input hand image and produce a prediction of the hand pose. Master networks and expert networks differ from each other based on the data on which they are trained. In particular, master networks are trained on the entire data set. In contrast, expert networks are trained only on a subset of the entire dataset. In regards to the hand poses, master networks are trained on the input image data representing all available hand poses comprising the training data (including both real and simulated hand images).

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