公开(公告)号：US12243238B1

公开(公告)日：2025-03-04

申请号：US18224551

申请日：2023-07-20

Applicant: ULTRAHAPTICS IP TWO LIMITED

Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

IPC: G06T7/13

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.

公开(公告)号：US11841920B1

公开(公告)日：2023-12-12

申请号：US15432869

申请日：2017-02-14

Applicant: Ultrahaptics IP Two Limited

Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

IPC: G06K9/62 ,  G06K9/00 ,  G06T7/73 ,  G06K9/78 ,  G06T7/285 ,  G06T7/246 ,  G06N3/04 ,  G06N3/08 ,  G06F3/01 ,  G06V10/10 ,  G06V20/64 ,  G06V40/20 ,  G06F18/214 ,  G06F18/24 ,  G06V10/70

CPC classification number: G06F18/214 ,  G06F18/24 ,  G06N3/04 ,  G06N3/08 ,  G06T7/248 ,  G06T7/285 ,  G06T7/74 ,  G06V10/70 ,  G06V20/64 ,  G06V40/28 ,  G06F3/011 ,  G06F3/017 ,  G06T2207/10021 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/30196

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).

公开(公告)号：US11798141B2

公开(公告)日：2023-10-24

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

CPC classification number: G06T5/006 ,  G06T19/006 ,  G06T19/20

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.

公开(公告)号：US11714880B1

公开(公告)日：2023-08-01

申请号：US16508231

申请日：2019-07-10

Applicant: Ultrahaptics IP Two Limited

Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

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

CPC classification number: G06K9/6269 ,  G06K9/00355 ,  G06K9/4604 ,  G06T7/13 ,  G06T2207/10028

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.

公开(公告)号：US10656720B1

公开(公告)日：2020-05-19

申请号：US14997454

申请日：2016-01-15

Applicant: Ultrahaptics IP Two Limited

Inventor： David Samuel Holz

IPC: G06F3/01 ,  G06T19/00

Abstract: The technology disclosed relates to user interfaces for controlling augmented reality (AR) or virtual reality (VR) environments. Real and virtual objects can be seamlessly integrated to form an augmented reality by tracking motion of one or more real objects within view of a wearable sensor system. Switching the AR/VR presentation on or off to interact with the real world surrounding them, for example to drink some soda, can be addressed with a convenient mode switching gesture associated with switching between operational modes in a VR/AR enabled device.