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公开(公告)号：US20210183089A1
公开(公告)日：2021-06-17
申请号：US16759808
申请日：2017-11-03
Applicant: Google LLC
Inventor： Neal Wadhwa , Jonathan Barron , Rahul Garg , Pratul Srinivasan
Abstract: Example embodiments allow for training of artificial neural networks (ANNs) to generate depth maps based on images. The ANNs are trained based on a plurality of sets of images, where each set of images represents a single scene and the images in such a set of images differ with respect to image aperture and/or focal distance. An untrained ANN generates a depth map based on one or more images in a set of images. This depth map is used to generate, using the image(s) in the set, a predicted image that corresponds, with respect to image aperture and/or focal distance, to one of the images in the set. Differences between the predicted image and the corresponding image are used to update the ANN. ANNs tramed in this manner are especially suited for generating depth maps used to perform simulated image blur on small-aperture images.)
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公开(公告)号：US10852847B2
公开(公告)日：2020-12-01
申请号：US15660216
申请日：2017-07-26
Applicant: GOOGLE LLC
Inventor： Joel Hesch , Shiqi Chen , Johnny Lee , Rahul Garg
IPC: G06F3/0346 , G06T7/73 , G06F3/0354 , G06F3/01
Abstract: A method for controller tracking with multiple degrees of freedom includes generating depth data at an electronic device based on a local environment proximate the electronic device. A set of positional data is generated for at least one spatial feature associated with a controller based on a pose of the electronic device, as determined using the depth data, relative to the at least one spatial feature associated with the controller. A set of rotational data is received that represents three degrees-of-freedom (3DoF) orientation of the controller within the local environment, and a six degrees-of-freedom (6DoF) position of the controller within the local environment is tracked based on the set of positional data and the set of rotational data.
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13.发明申请公开(公告)号：US20250126228A1
公开(公告)日：2025-04-17
申请号：US18916671
申请日：2024-10-15
Applicant: Google LLC
Inventor： Andrey Ryabtsev , Rahul Garg , Amelio Vázquez-Reina , Wonsik Kim , Robert Anderson , Weijuan Xi , Desai Fan , Fangda Li , Chun-Ting Liu
Abstract: A first video stream comprising a first image of a first participant of a virtual meeting, a second image of a second participant, and a third image of a third participant are received from a first client device connected to a virtual meeting platform. It is determined whether an image combining condition is satisfied. Responsive to determining that the image combining condition is satisfied with respect to the first image and the second image, a first screen tile comprising the first image and the second image is generated. A first size of the first screen tile is defined based on a number of images comprised by the first screen tile. A second screen tile comprising the third image is generated. A virtual meeting user interface comprising the first screen tile and the second screen tile is provided for presentation on a second client device connected to the virtual meeting platform.
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公开(公告)号：US20250047806A1
公开(公告)日：2025-02-06
申请号：US18229603
申请日：2023-08-02
Applicant: Google LLC
Inventor： Anne Menini , Jeya Maria Jose Valanarasu , Rahul Garg , Andeep Singh Toor , Xin Tong , Weijuan Xi
Abstract: Methods and systems for real-time video enhancement are provided herein. A current frame of a video stream generated by a client device of a plurality of client devices participating in the video conference is identified during a video conference. An enhanced previous frame corresponding to an enhanced version of a previous frame in the video stream is identified. At least the current frame and the enhanced previous frame are provided as input to a machine-learning model. An output of the machine learning model is obtained. The output of the machine learning model indicates an enhanced current frame corresponding to an enhanced version of the current frame. The current frame is replaced with the enhanced current frame in the video stream.
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公开(公告)号：US12033309B2
公开(公告)日：2024-07-09
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
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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Patent Agency Ranking
公开(公告)号：US12008738B2
公开(公告)日：2024-06-11
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：US11599747B2
公开(公告)日：2023-03-07
申请号：US17090948
申请日：2020-11-06
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：US20200242788A1
公开(公告)日：2020-07-30
申请号：US16652568
申请日：2017-12-05
Applicant: Google LLC
Inventor： David Jacobs , Rahul Garg , Yael Pritch Knaan , Neal Wadhwa , Marc Levoy
Abstract: A camera may capture an image of a scene and use the image to generate a first and a second subpixel image of the scene. The pair of subpixel images may be represented by a first set of subpixels and a second set of subpixels from the image respectively. Each pixel of the image may include two green subpixels that are respectively represented in the first and second subpixel images. The camera may determine a disparity between a portion of the scene as represented by the pair of subpixel images and may estimate a depth map of the scene that indicates a depth of the portion relative to other portions of the scene based on the disparity and a baseline distance between the two green subpixels. A new version of the image may be generated with a focus upon the portion and with the other portions of the scene blurred.
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公开(公告)号：US12118697B2
公开(公告)日：2024-10-15
申请号：US17753279
申请日：2021-02-24
Applicant: Google LLC
Inventor： Rahul Garg , Neal Wadhwa
IPC: G06T5/73 , G06T5/50 , H04N25/704
CPC classification number: G06T5/73 , G06T5/50 , H04N25/704
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：US11210799B2
公开(公告)日：2021-12-28
申请号：US16652568
申请日：2017-12-05
Applicant: Google LLC
Inventor： David Jacobs , Rahul Garg , Yael Pritch Knaan , Neal Wadhwa , Marc Levoy
Abstract: A camera may capture an image of a scene and use the image to generate a first and a second subpixel image of the scene. The pair of subpixel images may be represented by a first set of subpixels and a second set of subpixels from the image respectively. Each pixel of the image may include two green subpixels that are respectively represented in the first and second subpixel images. The camera may determine a disparity between a portion of the scene as represented by the pair of subpixel images and may estimate a depth map of the scene that indicates a depth of the portion relative to other portions of the scene based on the disparity and a baseline distance between the two green subpixels. A new version of the image may be generated with a focus upon the portion and with the other portions of the scene blurred.
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