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公开(公告)号：US11308659B2
公开(公告)日：2022-04-19
申请号：US17390263
申请日：2021-07-30
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Seyed Mohammad Mehdi Sajjadi , Jonathan Tilton Barron , Noha Radwan , Alexey Dosovitskiy , Ricardo Martin-Brualla
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
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公开(公告)号：US20190124319A1
公开(公告)日：2019-04-25
申请号：US16226643
申请日：2018-12-20
Applicant: Google LLC
Inventor： Jonathan Tilton Barron , Stephen Joseph DiVerdi , Ryan Geiss
IPC: H04N13/239 , G06K9/62 , H04N5/235 , G06T5/00 , G06T5/10 , G06T7/269 , H04N13/271 , H04N9/09 , G06T7/38 , G06T7/30 , G06T7/292
Abstract: The present disclosure relates to methods and systems that may improve and/or modify images captured using multiscopic image capture systems. In an example embodiment, burst image data is captured via a multiscopic image capture system. The burst image data may include at least one image pair. The at least one image pair is aligned based on at least one rectifying homography function. The at least one aligned image pair is warped based on a stereo disparity between the respective images of the image pair. The warped and aligned images are then stacked and a denoising algorithm is applied. Optionally, a high dynamic range algorithm may be applied to at least one output image of the aligned, warped, and denoised images.
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公开(公告)号：US20250037244A1
公开(公告)日：2025-01-30
申请号：US18709218
申请日：2022-10-21
Applicant: Google LLC
Inventor： Benjamin Joseph Mildenhall , Pratul Preeti Srinivasan , Jonathan Tilton Barron , Richardo Martin-Brualla , Lars Peter Johannes Hedman
Abstract: Systems and methods for training a neural radiance field model for noisy scenes can leverage raw noisy images in linear high dynamic range color space to train a neural radiance field model to generate view synthesis of low light and/or high contrast scenes. The trained model can then be utilized to accurately complete view rendering tasks without the preprocessing used for generating low dynamic range images. In some implementations, training on unprocessed data of a low light scene can allow for training a neural radiance field model to generate high quality view renderings of a low light scene.
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公开(公告)号：US20250014236A1
公开(公告)日：2025-01-09
申请号：US18891789
申请日：2024-09-20
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Alexey Dosovitskiy , Ricardo Martin-Brualla , Jonathan Tilton Barron , Noha Radwan , Seyed Mohammad Mehdi Sajjadi
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
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公开(公告)号：US12100074B2
公开(公告)日：2024-09-24
申请号：US18327609
申请日：2023-06-01
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Alexey Dosovitskiy , Ricardo Martin-Brualla , Jonathan Tilton Barron , Noha Radwan , Seyed Mohammad Mehdi Sajjadi
CPC classification number: G06T11/001 , G06T7/90 , G06T2207/20081
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
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Patent Agency Ranking
公开(公告)号：US20230360182A1
公开(公告)日：2023-11-09
申请号：US18028930
申请日：2021-05-17
Applicant: Google LLC
Inventor： Sean Ryan Francesco Fanello , Yun-Ta Tsai , Rohit Kumar Pandey , Paul Debevec , Michael Milne , Chloe LeGendre , Jonathan Tilton Barron , Christoph Rhemann , Sofien Bouaziz , Navin Padman Sarma
CPC classification number: G06T5/009 , G06T7/60 , G06T7/70 , G06T15/506 , G06T2200/24 , G06T2207/20081 , G06T2207/20084 , G06T2207/20092 , G06T2207/20208 , G06T2207/30201
Abstract: Apparatus and methods related to applying lighting models to images of objects are provided. An example method includes applying a geometry model to an input image to determine a surface orientation map indicative of a distribution of lighting on an object based on a surface geometry. The method further includes applying an environmental light estimation model to the input image to determine a direction of synthetic lighting to be applied to the input image. The method also includes applying, based on the surface orientation map and the direction of synthetic lighting, a light energy model to determine a quotient image indicative of an amount of light energy to be applied to each pixel of the input image. The method additionally includes enhancing, based on the quotient image, a portion of the input image. One or more neural networks can be trained to perform one or more of the aforementioned aspects.
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公开(公告)号：US20230306655A1
公开(公告)日：2023-09-28
申请号：US18327609
申请日：2023-06-01
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Alexey Dosovitskiy , Ricardo Martin-Brualla , Jonathan Tilton Barron , Noha Radwan , Seyed Mohammad Mehdi Sajjadi
CPC classification number: G06T11/001 , G06T7/90 , G06T2207/20081
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
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18.发明公开公开(公告)号：US20230281913A1
公开(公告)日：2023-09-07
申请号：US17686683
申请日：2022-03-04
Applicant: Google LLC
Inventor： Konstantinos Rematas , Thomas Allen Funkhouser , Vittorio Carlo Ferrari , Andrew Huaming Liu , Andrea Tagliasacchi , Pratul Preeti Srinivasan , Jonathan Tilton Barron
CPC classification number: G06T15/205 , G06T17/10 , G06N20/00 , G06T5/50 , G06T5/009
Abstract: Systems and methods for view synthesis and three-dimensional reconstruction can learn an environment by utilizing a plurality of images of an environment and depth data. The use of depth data can be helpful when the quantity of images and different angles may be limited. For example, large outdoor environments can be difficult to learn due to the size, the varying image exposures, and the limited variance in view direction changes. The systems and methods can leverage a plurality of panoramic images and corresponding lidar data to accurately learn a large outdoor environment to then generate view synthesis outputs and three-dimensional reconstruction outputs. Training may include the use of an exposure correction network to address lighting exposure differences between training images.
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公开(公告)号：US20220036602A1
公开(公告)日：2022-02-03
申请号：US17390263
申请日：2021-07-30
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Seyed Mohammad Mehdi Sajjadi , Jonathan Tilton Barron , Noha Waheed Ahmed Radwan , Alexey Dosovitskiy , Ricardo Martin-Brualla
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
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公开(公告)号：US20200084429A1
公开(公告)日：2020-03-12
申请号：US16680474
申请日：2019-11-11
Applicant: Google LLC
Inventor： Jonathan Tilton Barron , Stephen Joseph DiVerdi , Ryan Geiss
IPC: H04N13/239 , G06T7/292 , G06T7/269 , H04N9/09 , G06T5/10 , G06T5/00 , G06K9/62 , H04N13/271 , G06T7/30 , H04N5/235 , G06T7/38 , H04N13/25
Abstract: The present disclosure relates to methods and systems that may improve and/or modify images captured using multiscopic image capture systems. In an example embodiment, burst image data is captured via a multiscopic image capture system. The burst image data may include at least one image pair. The at least one image pair is aligned based on at least one rectifying homography function. The at least one aligned image pair is warped based on a stereo disparity between the respective images of the image pair. The warped and aligned images are then stacked and a denoising algorithm is applied. Optionally, a high dynamic range algorithm may be applied to at least one output image of the aligned, warped, and denoised images.
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