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    INTUITIVE EDITING OF THREE-DIMENSIONAL MODELS
    3.
    发明申请
    INTUITIVE EDITING OF THREE-DIMENSIONAL MODELS 有权

    公开(公告)号：US20210256775A1

    公开(公告)日：2021-08-19

    申请号：US17208627

    申请日：2021-03-22

    申请人： ADOBE INC.

    发明人： Duygu Ceylan Aksit ,  Vladimir Kim ,  Siddhartha Chaudhuri ,  Radomir Mech ,  Noam Aigerman ,  Kevin Wampler ,  Jonathan Eisenmann ,  Giorgio Gori ,  Emiliano Gambaretto

    IPC分类号： G06T19/20

    摘要： Embodiments of the present invention are directed towards intuitive editing of three-dimensional models. In embodiments, salient geometric features associated with a three-dimensional model defining an object are identified. Thereafter, feature attributes associated with the salient geometric features are identified. A feature set including a plurality of salient geometric features related to one another is generated based on the determined feature attributes (e.g., properties, relationships, distances). An editing handle can then be generated and displayed for the feature set enabling each of the salient geometric features within the feature set to be edited in accordance with a manipulation of the editing handle. The editing handle can be displayed in association with one of the salient geometric features of the feature set.

    Learning to estimate high-dynamic range outdoor lighting parameters
    4.
    发明授权
    Learning to estimate high-dynamic range outdoor lighting parameters 有权

    公开(公告)号：US10936909B2

    公开(公告)日：2021-03-02

    申请号：US16188130

    申请日：2018-11-12

    申请人： ADOBE INC.

    发明人： Kalyan K. Sunkavalli ,  Sunil Hadap ,  Jonathan Eisenmann ,  Jinsong Zhang ,  Emiliano Gambaretto

    IPC分类号： G06K9/62 ,  G06K9/46 ,  G06T5/00

    摘要： 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.

    NEURAL NETWORK-BASED CAMERA CALIBRATION
    6.
    发明申请
    NEURAL NETWORK-BASED CAMERA CALIBRATION 审中-公开

    公开(公告)号：US20200074682A1

    公开(公告)日：2020-03-05

    申请号：US16675641

    申请日：2019-11-06

    申请人： ADOBE INC.

    发明人： Kalyan K. Sunkavalli ,  Yannick Hold-Geoffroy ,  Sunil Hadap ,  Matthew David Fisher ,  Jonathan Eisenmann ,  Emiliano Gambaretto

    IPC分类号： G06T7/80 ,  G06N3/08 ,  G06T7/00 ,  G06N3/04

    摘要： 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.

    Refining local parameterizations for applying two-dimensional images to three-dimensional models
    7.
    发明授权
    Refining local parameterizations for applying two-dimensional images to three-dimensional models 有权

    公开(公告)号：US10521970B2

    公开(公告)日：2019-12-31

    申请号：US15900864

    申请日：2018-02-21

    申请人： Adobe Inc.

    发明人： Emiliano Gambaretto ,  Vladimir Kim ,  Qingnan Zhou ,  Mehmet Ersin Yumer

    IPC分类号： G06T17/20 ,  G06T19/20

    摘要： Certain embodiments involve refining local parameterizations that apply two-dimensional (“2D”) images to three-dimensional (“3D”) models. For instance, a particular parameterization-initialization process is select based on one or more features of a target mesh region. An initial local parameterization for a 2D image is generated from this parameterization-initialization process. A quality metric for the initial local parameterization is computed, and the local parameterization is modified to improve the quality metric. The 3D model is modified by applying image points from the 2D image to the target mesh region in accordance with the modified local parameterization.

    Neural network-based camera calibration
    8.
    发明授权
    Neural network-based camera calibration 有权

    公开(公告)号：US10515460B2

    公开(公告)日：2019-12-24

    申请号：US15826331

    申请日：2017-11-29

    申请人： ADOBE INC.

    发明人： Kalyan K. Sunkavalli ,  Yannick Hold-Geoffroy ,  Sunil Hadap ,  Matthew David Fisher ,  Jonathan Eisenmann ,  Emiliano Gambaretto

    IPC分类号： G06T7/80 ,  G06N3/08 ,  G06T7/00

    摘要： 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.

    High dynamic range illumination estimation
    9.
    发明授权
    High dynamic range illumination estimation 有权

    公开(公告)号：US10475169B2

    公开(公告)日：2019-11-12

    申请号：US15824943

    申请日：2017-11-28

    申请人： Adobe Inc.

    发明人： Kalyan Sunkavalli ,  Mehmet Ersin Yumer ,  Marc-Andre Gardner ,  Xiaohui Shen ,  Jonathan Eisenmann ,  Emiliano Gambaretto

    IPC分类号： G06T5/00 ,  G06T9/00 ,  G06T7/90 ,  G06T1/00

    摘要： 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.