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公开(公告)号：US20220300082A1
公开(公告)日：2022-09-22
申请号：US17249966
申请日：2021-03-19
Applicant: GOOGLE LLC
Inventor： Dongeek Shin , Shahram Izadi , David Kim , Sofien Bouaziz , Steven Benjamin Goldberg , Ivan Poupyrev , Shwetak N. Patel
Abstract: Techniques of identifying gestures include detecting and classifying inner-wrist muscle motions at a user's wrist using micron-resolution radar sensors. For example, a user of an AR system may wear a band around their wrist. When the user makes a gesture to manipulate a virtual object in the AR system as seen in a head-mounted display (HMD), muscles and ligaments in the user's wrist make small movements on the order of 1-3 mm. The band contains a small radar device that has a transmitter and a number of receivers (e.g., three) of electromagnetic (EM) radiation on a chip (e.g., a Soli chip. This radiation reflects off the wrist muscles and ligaments and is received by the receivers on the chip in the band. The received reflected signal, or signal samples, are then sent to processing circuitry for classification to identify the wrist movement as a gesture.
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Patent Agency Ranking
公开(公告)号：US20220295443A1
公开(公告)日：2022-09-15
申请号：US17196093
申请日：2021-03-09
Applicant: Google LLC
Inventor： Dongeek Shin , Steven Benjamin Goldberg , Shahram Izadi
Abstract: This document describes systems and techniques to generate refined location estimates using ultra-wideband (UWB) communication links. Mobile devices, such as smartphones, include location sensors to estimate their location. The accuracy of location sensors is generally about 3 meters (or about 10 feet). More accurate location data would allow mobile devices to provide new and improved functionality. The described systems and techniques determine the distance between nearby mobile devices using UWB communication links. A mobile device can then use the distance between the mobile devices to determine their relative locations. By comparing the relative locations of the mobile devices with their location estimates, the mobile device can generate a refined location estimate with greater accuracy.
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公开(公告)号：US20220191374A1
公开(公告)日：2022-06-16
申请号：US17439762
申请日：2019-09-25
Applicant: Google LLC
Inventor： Adarsh Prakash Murthy Kowdle , Ruben Manuel Velarde , Zhijun He , Xu Han , Kourosh Derakshan , Shahram Izadi
Abstract: This document describes techniques and systems that enable automatic exposure and gain control for face authentication. The techniques and systems include a user device initializing a gain for a near-infrared camera system using a default gain. The user device ascertains patch-mean statistics of one or more regions-of-interest of a most-recently captured image that was captured by the near-infrared camera system. The user device computes an update in the initialized gain to provide an updated gain that is usable to scale the one or more regions-of-interest toward a target mean-luminance value. The user device dampens the updated gain by using hysteresis. Then, the user device sets the initialized gain for the near-infrared camera system to the dampened updated gain.
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34.发明授权公开(公告)号：US10997457B2
公开(公告)日：2021-05-04
申请号：US16616235
申请日：2019-10-16
Applicant: Google LLC
Inventor： Christoph Rhemann , Abhimitra Meka , Matthew Whalen , Jessica Lynn Busch , Sofien Bouaziz , Geoffrey Douglas Harvey , Andrea Tagliasacchi , Jonathan Taylor , Paul Debevec , Peter Joseph Denny , Sean Ryan Francesco Fanello , Graham Fyffe , Jason Angelo Dourgarian , Xueming Yu , Adarsh Prakash Murthy Kowdle , Julien Pascal Christophe Valentin , Peter Christopher Lincoln , Rohit Kumar Pandey , Christian Häne , Shahram Izadi
Abstract: Methods, systems, and media for relighting images using predicted deep reflectance fields are provided. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample includes (i) a group of one-light-at-a-time (OLAT) images that have each been captured when one light of a plurality of lights arranged on a lighting structure has been activated, (ii) a group of spherical color gradient images that have each been captured when the plurality of lights arranged on the lighting structure have been activated to each emit a particular color, and (iii) a lighting direction, wherein each image in the group of OLAT images and each of the spherical color gradient images are an image of a subject, and wherein the lighting direction indicates a relative orientation of a light to the subject; training a convolutional neural network using the group of training samples, wherein training the convolutional neural network comprises: for each training iteration in a series of training iterations and for each training sample in the group of training samples: generating an output predicted image, wherein the output predicted image is a representation of the subject associated with the training sample with lighting from the lighting direction associated with the training sample; identifying a ground-truth OLAT image included in the group of OLAT images for the training sample that corresponds to the lighting direction for the training sample; calculating a loss that indicates a perceptual difference between the output predicted image and the identified ground-truth OLAT image; and updating parameters of the convolutional neural network based on the calculated loss; identifying a test sample that includes a second group of spherical color gradient images and a second lighting direction; and generating a relit image of the subject included in each of the second group of spherical color gradient images with lighting from the second lighting direction using the trained convolutional neural network.
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公开(公告)号：US10554957B2
公开(公告)日：2020-02-04
申请号：US15996880
申请日：2018-06-04
Applicant: Google LLC
Inventor： Julien Pascal Christophe Valentin , Sean Ryan Fanello , Adarsh Prakash Murthy Kowdle , Christoph Rhemann , Vladimir Tankovich , Philip L. Davidson , Shahram Izadi
IPC: G06K9/64 , H04N13/271 , H04N19/597 , H04N13/128 , G06K9/62 , G06T7/593 , H04N13/00
Abstract: A first and second image of a scene are captured. Each of a plurality of pixels in the first image is associated with a disparity value. An image patch associated with each of the plurality of pixels of the first image and the second image is mapped into a binary vector. Thus, values of pixels in an image are mapped to a binary space using a function that preserves characteristics of values of the pixels. The difference between the binary vector associated with each of the plurality of pixels of the first image and its corresponding binary vector in the second image designated by the disparity value associated with each of the plurality of pixels of the first image is determined. Based on the determined difference between binary vectors, correspondence between the plurality of pixels of the first image and the second image is established.
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公开(公告)号：US20180352213A1
公开(公告)日：2018-12-06
申请号：US15996880
申请日：2018-06-04
Applicant: Google LLC
Inventor： Julien Pascal Christophe Valentin , Sean Ryan Fanello , Adarsh Prakash Murthy Kowdle , Christoph Rhemann , Vladimir Tankovich , Philip L. Davidson , Shahram Izadi
IPC: H04N13/271 , H04N19/597 , G06K9/62 , H04N13/128
CPC classification number: H04N13/271 , G06K9/6268 , G06T7/593 , G06T2207/20081 , H04N13/128 , H04N19/597 , H04N2013/0081
Abstract: A first and second image of a scene are captured. Each of a plurality of pixels in the first image is associated with a disparity value. An image patch associated with each of the plurality of pixels of the first image and the second image is mapped into a binary vector. Thus, values of pixels in an image are mapped to a binary space using a function that preserves characteristics of values of the pixels. The difference between the binary vector associated with each of the plurality of pixels of the first image and its corresponding binary vector in the second image designated by the disparity value associated with each of the plurality of pixels of the first image is determined. Based on the determined difference between binary vectors, correspondence between the plurality of pixels of the first image and the second image is established.
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