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公开(公告)号：US11983854B2
公开(公告)日：2024-05-14
申请号：US17093852
申请日：2020-11-10
Applicant: Adobe Inc.
Inventor： Mustafa Isik , Michael Yanis Gharbi , Matthew David Fisher , Krishna Bhargava Mullia Lakshminarayana , Jonathan Eisenmann , Federico Perazzi
Abstract: A plurality of pixel-based sampling points are identified within an image, wherein sampling points of a pixel are distributed within the pixel. For individual sampling points of individual pixels, a corresponding radiance vector is estimated. A radiance vector includes one or more radiance values characterizing light received at a sampling point. A first machine learning module generates, for each pixel, a corresponding intermediate radiance feature vector, based on the radiance vectors associated with the sampling points within that pixel. A second machine learning module generates, for each pixel, a corresponding final radiance feature vector, based on an intermediate radiance feature vector for that pixel, and one or more other intermediate radiance feature vectors for one or more other pixels neighboring that pixel. One or more kernels are generated, based on the final radiance feature vectors, and applied to corresponding pixels of the image, to generate a lower noise image.
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公开(公告)号：US11443412B2
公开(公告)日：2022-09-13
申请号：US16678072
申请日：2019-11-08
Applicant: Adobe Inc.
Inventor： Kalyan Sunkavalli , Mehmet Ersin Yumer , Marc-Andre Gardner , Xiaohui Shen , Jonathan Eisenmann , Emiliano Gambaretto
Abstract: Systems and techniques for estimating illumination from a single image are provided. An example system may include a neural network. The neural network may include an encoder that is configured to encode an input image into an intermediate representation. The neural network may also include an intensity decoder that is configured to decode the intermediate representation into an output light intensity map. An example intensity decoder is generated by a multi-phase training process that includes a first phase to train a light mask decoder using a set of low dynamic range images and a second phase to adjust parameters of the light mask decoder using a set of high dynamic range image to generate the intensity decoder.
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公开(公告)号：US10964060B2
公开(公告)日：2021-03-30
申请号：US16675641
申请日：2019-11-06
Applicant: ADOBE INC.
Inventor： Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Matthew David Fisher , Jonathan Eisenmann , Emiliano Gambaretto
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.
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公开(公告)号：US20200302579A1
公开(公告)日：2020-09-24
申请号：US16893505
申请日：2020-06-05
Applicant: Adobe Inc.
Inventor： Jonathan Eisenmann , Zhe Lin , Matthew Fisher
Abstract: In some embodiments, an image manipulation application receives a two-dimensional background image and projects the background image onto a sphere to generate a sphere image. Based on the sphere image, an unfilled environment map containing a hole area lacking image content can be generated. A portion of the unfilled environment map can be projected to an unfilled projection image using a map projection. The unfilled projection image contains the hole area. A hole filling model is applied to the unfilled projection image to generate a filled projection image containing image content for the hole area. A filled environment map can be generated by applying an inverse projection of the map projection on the filled projection image and by combining the unfilled environment map with the generated image content for the hole area of the environment map.
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公开(公告)号：US20200151509A1
公开(公告)日：2020-05-14
申请号：US16188130
申请日：2018-11-12
Applicant: ADOBE INC.
Inventor： Kalyan K. Sunkavalli , Sunil Hadap , Jonathan Eisenmann , Jinsong Zhang , Emiliano Gambaretto
Abstract: Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate lighting parameters for input images where the input images are synthetic and real low-dynamic range images. Such a neural network system can be trained using differences between a simple scene rendered using the estimated lighting parameters and the same simple scene rendered using known ground-truth lighting parameters. Such a neural network system can also be trained such that the synthetic and real low-dynamic range images are mapped in roughly the same distribution. Such a trained neural network system can be used to input a low-dynamic range image determine high-dynamic range lighting parameters.
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Patent Agency Ranking
6.发明授权公开(公告)号：US11972534B2
公开(公告)日：2024-04-30
申请号：US17519841
申请日：2021-11-05
Applicant: Adobe Inc.
IPC: G06T19/20 , G06F18/211 , G06F18/22 , G06N3/02 , G06T15/04
CPC classification number: G06T19/20 , G06F18/211 , G06F18/22 , G06N3/02 , G06T15/04 , G06T2219/2004 , G06T2219/2016
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a visual neural network to replace materials in a three-dimensional scene with visually similar materials from a source dataset. Specifically, the disclosed system utilizes the visual neural network to generate source deep visual features representing source texture maps from materials in a plurality of source materials. Additionally, the disclosed system utilizes the visual neural network to generate deep visual features representing texture maps from materials in a digital scene. The disclosed system then determines source texture maps that are visually similar to the texture maps of the digital scene based on visual similarity metrics that compare the source deep visual features and the deep visual features. Additionally, the disclosed system modifies the digital scene by replacing one or more of the texture maps in the digital scene with the visually similar source texture maps.
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7.发明公开公开(公告)号：US20240127402A1
公开(公告)日：2024-04-18
申请号：US18238290
申请日：2023-08-25
Applicant: Adobe Inc.
Inventor： Mohammad Reza Karimi Dastjerdi , Yannick Hold-Geoffroy , Sai Bi , Jonathan Eisenmann , Jean-François Lalonde
CPC classification number: G06T5/50 , G06T5/002 , G06T15/506 , G06T2200/24 , G06T2207/20081 , G06T2207/20084 , G06T2207/20092 , G06T2207/20208
Abstract: In some examples, a computing system accesses a field of view (FOV) image that has a field of view less than 360 degrees and has low dynamic range (LDR) values. The computing system estimates lighting parameters from a scene depicted in the FOV image and generates a lighting image based on the lighting parameters. The computing system further generates lighting features generated the lighting image and image features generated from the FOV image. These features are aggregated into aggregated features and a machine learning model is applied to the image features and the aggregated features to generate a panorama image having high dynamic range (HDR) values.
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8.发明公开公开(公告)号：US20230141395A1
公开(公告)日：2023-05-11
申请号：US17519841
申请日：2021-11-05
Applicant: Adobe Inc.
CPC classification number: G06T19/20 , G06K9/6215 , G06K9/6228 , G06N3/02 , G06T15/04 , G06T2219/2004 , G06T2219/2016
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a visual neural network to replace materials in a three-dimensional scene with visually similar materials from a source dataset. Specifically, the disclosed system utilizes the visual neural network to generate source deep visual features representing source texture maps from materials in a plurality of source materials. Additionally, the disclosed system utilizes the visual neural network to generate deep visual features representing texture maps from materials in a digital scene. The disclosed system then determines source texture maps that are visually similar to the texture maps of the digital scene based on visual similarity metrics that compare the source deep visual features and the deep visual features. Additionally, the disclosed system modifies the digital scene by replacing one or more of the texture maps in the digital scene with the visually similar source texture maps.
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公开(公告)号：US20210073955A1
公开(公告)日：2021-03-11
申请号：US16564398
申请日：2019-09-09
Applicant: ADOBE INC.
Inventor： Jinsong Zhang , Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Jonathan Eisenmann , Jean-Francois Lalonde
IPC: G06T5/00
Abstract: Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate high-dynamic range lighting parameters for input low-dynamic range images. The high-dynamic range lighting parameters can be based on sky color, sky turbidity, sun color, sun shape, and sun position. Such input low-dynamic range images can be low-dynamic range panorama images or low-dynamic range standard images. Such a neural network system can apply the estimates high-dynamic range lighting parameters to objects added to the low-dynamic range images.
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公开(公告)号：US20190164261A1
公开(公告)日：2019-05-30
申请号：US15824943
申请日：2017-11-28
Applicant: Adobe Inc.
Inventor： Kalyan Sunkavalli , Mehmet Ersin Yumer , Marc-Andre Gardner , Xiaohui Shen , Jonathan Eisenmann , Emiliano Gambaretto
CPC classification number: G06T5/007 , G06N3/0454 , G06N3/082 , G06T1/0007 , G06T1/20 , G06T7/90 , G06T9/002 , G06T2207/10024 , G06T2207/10152 , G06T2215/12
Abstract: Systems and techniques for estimating illumination from a single image are provided. An example system may include a neural network. The neural network may include an encoder that is configured to encode an input image into an intermediate representation. The neural network may also include an intensity decoder that is configured to decode the intermediate representation into an output light intensity map. An example intensity decoder is generated by a multi-phase training process that includes a first phase to train a light mask decoder using a set of low dynamic range images and a second phase to adjust parameters of the light mask decoder using a set of high dynamic range image to generate the intensity decoder.
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