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    Methods, systems, and media for relighting images using predicted deep reflectance fields
    1.
    发明授权
    Methods, systems, and media for relighting images using predicted deep reflectance fields 有权

    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

    IPC: G06K9/46 ,  G06T15/50 ,  G06T15/20 ,  G06N3/08 ,  G06K9/62

    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.

    Deep light design
    4.
    发明授权
    Deep light design 有权

    公开(公告)号：US12165380B2

    公开(公告)日：2024-12-10

    申请号：US17309206

    申请日：2019-11-15

    Applicant: GOOGLE LLC

    Inventor： Chloe LeGendre ,  Wan-Chun Ma ,  Graham Fyffe ,  John Flynn ,  Jessica Busch ,  Paul Debevec

    IPC: G06V10/60 ,  G06V10/44 ,  G06V10/75 ,  G06V20/40

    Abstract: An example method, apparatus, and computer-readable storage medium are provided to predict high-dynamic range (HDR) lighting from low-dynamic range (LDR) background images. In an example implementation, a method may include receiving low-dynamic range (LDR) background images of scenes, each LDR background image captured with appearance of one or more reference objects with different reflectance properties; and training a lighting estimation model based at least on the received LDR background images to predict high-dynamic range (HDR) lighting based at least on the trained model. In another example implementation, a method may include capturing a low-dynamic range (LDR) background image of a scene from an LDR video captured by a camera of the electronic computing device; predicting high-dynamic range (HDR) lighting for the image, the predicting, using a trained model, based at least on the LDR background image; and rendering a virtual object based at least on the predicted HDR lighting.

    DEEP LIGHT DESIGN
    5.
    发明申请
    DEEP LIGHT DESIGN 有权

    公开(公告)号：US20210406581A1

    公开(公告)日：2021-12-30

    申请号：US17309206

    申请日：2019-11-15

    Applicant: GOOGLE LLC

    Inventor： Chloe LeGendre ,  Wan-Chun Ma ,  Graham Fyffe ,  John Flynn ,  Jessica Busch ,  Paul Debevec

    IPC: G06K9/46 ,  G06K9/00 ,  G06K9/62

    Abstract: An example method, apparatus, and computer-readable storage medium are provided to predict high-dynamic range (HDR) lighting from low-dynamic range (LDR) background images. In an example implementation, a method may include receiving low-dynamic range (LDR) background images of scenes, each LDR background image captured with appearance of one or more reference objects with different reflectance properties; and training a lighting estimation model based at least on the received LDR background images to predict high-dynamic range (HDR) lighting based at least on the trained model. In another example implementation, a method may include capturing a low-dynamic range (LDR) background image of a scene from an LDR video captured by a camera of the electronic computing device; predicting high-dynamic range (HDR) lighting for the image, the predicting, using a trained model, based at least on the LDR background image; and rendering a virtual object based at least on the predicted HDR lighting.

    METHODS, SYSTEMS, AND MEDIA FOR RELIGHTING IMAGES USING PREDICTED DEEP REFLECTANCE FIELDS
    6.
    发明申请
    METHODS, SYSTEMS, AND MEDIA FOR RELIGHTING IMAGES USING PREDICTED DEEP REFLECTANCE FIELDS 审中-公开

    公开(公告)号：US20200372284A1

    公开(公告)日：2020-11-26

    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

    IPC: G06K9/46 ,  G06T15/50 ,  G06K9/62 ,  G06N3/08 ,  G06T15/20

    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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