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公开(公告)号：US20240320808A1
公开(公告)日：2024-09-26
申请号：US18734000
申请日：2024-06-05
Applicant: Google LLC
Inventor： Yicheng Wu , Qiurui He , Tianfan Xue , Rahul Garg , Jiawen Chen , Jonathan T. Barron
CPC classification number: G06T5/80 , G06T3/40 , G06T5/10 , G06T5/20 , G06T7/80 , G06T2207/20081 , G06T2207/20084
Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.
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公开(公告)号：US20220375045A1
公开(公告)日：2022-11-24
申请号：US17625994
申请日：2020-11-09
Applicant: Google LLC
Inventor： Yicheng Wu , Qiurui He , Tianfan Xue , Rahul Garg , Jiawen Chen , Jonathan T. Barron
Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.
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公开(公告)号：US20220375042A1
公开(公告)日：2022-11-24
申请号：US17626069
申请日：2020-11-13
Applicant: Google LLC
Inventor： Rahul Garg , Neal Wadhwa , Pratul Preeti Srinivasan , Tianfan Xue , Jiawen Chen , Shumian Xin , Jonathan T. Barron
Abstract: A method includes obtaining dual-pixel image data that includes a first sub-image and a second sub-image, and generating an in-focus image, a first kernel corresponding to the first sub-image, and a second kernel corresponding to the second sub-image. A loss value may be determined using a loss function that determines a difference between (i) a convolution of the first sub-image with the second kernel and (ii) a convolution of the second sub-image with the first kernel, and/or a sum of (i) a difference between the first sub-image and a convolution of the in-focus image with the first kernel and (ii) a difference between the second sub-image and a convolution of the in-focus image with the second kernel. Based on the loss value and the loss function, the in-focus image, the first kernel, and/or the second kernel, may be updated and displayed.
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公开(公告)号：US11181986B2
公开(公告)日：2021-11-23
申请号：US16947833
申请日：2020-08-19
Applicant: GOOGLE LLC
Inventor： Shiqi Chen , Jonathan Tompson , Rahul Garg
Abstract: Systems and methods for context-sensitive hand interaction with an immersive environment are provided. An example method includes determining a contextual factor for a user and selecting an interaction mode based on the contextual factor. The example method may also include monitoring a hand of the user to determine a hand property and determining an interaction with an immersive environment based on the interaction mode and the hand property.
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公开(公告)号：US10782793B2
公开(公告)日：2020-09-22
申请号：US16100748
申请日：2018-08-10
Applicant: GOOGLE LLC
Inventor： Shiqi Chen , Jonathan Tompson , Rahul Garg
Abstract: Systems and methods for context-sensitive hand interaction with an immersive environment are provided. An example method includes determining a contextual factor for a user and selecting an interaction mode based on the contextual factor. The example method may also include monitoring a hand of the user to determine a hand property and determining an interaction with an immersive environment based on the interaction mode and the hand property.
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Patent Agency Ranking
公开(公告)号：US10139917B1
公开(公告)日：2018-11-27
申请号：US15263143
申请日：2016-09-12
Applicant: Google LLC
Inventor： Mehul Nariyawala , Rahul Garg , Navneet Dalal , Thor Carpenter , Gregory Burgess , Timothy Psiaki , Mark Chang , Antonio Bernardo Monteiro Costa , Christian Plagemann , Chee Chew
Abstract: Systems and methods are disclosed for gesture-initiated actions in videoconferences. In one implementation, a processing device receives content streams during a communication session, identifies a request for feedback within one of the content streams, based on an identification of the request for feedback, processes the content streams to identify one or more gestures within at least one of the content streams, and based on a determination that a first gesture of the one or more gestures is relatively more prevalent across the content streams than one or more other gestures, initiates an action with respect to the communication session.
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公开(公告)号：US20230153960A1
公开(公告)日：2023-05-18
申请号：US17753279
申请日：2021-02-24
Applicant: Google LLC
Inventor： Rahul Garg , Neal Wadhwa
Abstract: A method includes obtaining split-pixel image data including a first sub-image and a second sub-image. The method also includes determining, for each respective pixel of the split-pixel image data, a corresponding position of a scene feature represented by the respective pixel relative to a depth of field, and identifying, based on the corresponding positions, out-of-focus pixels. The method additionally includes determining, for each respective out-of-focus pixel, a corresponding pixel value based on the corresponding position, a location of the respective out-of-focus pixel within the split-pixel image data, and at least one of: a first value of a corresponding first pixel in the first sub-image or a second value of a corresponding second pixel in the second sub-image. The method further includes generating, based on the corresponding pixel values, an enhanced image having an extended depth of field.
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公开(公告)号：US10860889B2
公开(公告)日：2020-12-08
申请号：US16246280
申请日：2019-01-11
Applicant: Google LLC
Inventor： Yael Pritch Knaan , Marc Levoy , Neal Wadhwa , Rahul Garg , Sameer Ansari , Jiawen Chen
IPC: G06K9/62
Abstract: Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.
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公开(公告)号：US20190050062A1
公开(公告)日：2019-02-14
申请号：US16100748
申请日：2018-08-10
Applicant: GOOGLE LLC
Inventor： Shiqi Chen , Jonathan Tompson , Rahul Garg
Abstract: Systems and methods for context-sensitive hand interaction with an immersive environment are provided. An example method includes determining a contextual factor for a user and selecting an interaction mode based on the contextual factor. The example method may also include monitoring a hand of the user to determine a hand property and determining an interaction with an immersive environment based on the interaction mode and the hand property.
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公开(公告)号：US20230037958A1
公开(公告)日：2023-02-09
申请号：US17786065
申请日：2020-12-24
Applicant: GOOGLE LLC
Inventor： Orly Liba , Rahul Garg , Neal Wadhwa , Jon Barron , Hayato Ikoma
IPC: G06T7/50
Abstract: A system includes a computing device. The computing device is configured to perform a set of functions. The set of functions includes receiving an image, wherein the image comprises a two-dimensional array of data. The set of functions includes extracting, by a two-dimensional neural network, a plurality of two-dimensional features from the two-dimensional array of data. The set of functions includes generating a linear combination of the plurality of two-dimensional features to form a single three-dimensional input feature. The set of functions includes extracting, by a three-dimensional neural network, a plurality of three-dimensional features from the single three-dimensional input feature. The set of functions includes determining a two-dimensional depth map. The two-dimensional depth map contains depth information corresponding to the plurality of three-dimensional features.
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