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公开(公告)号：US12106428B2
公开(公告)日：2024-10-01
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 , G06N20/00 , G06T5/50 , G06T5/92 , G06T17/10
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：US11704844B2
公开(公告)日：2023-07-18
申请号：US17722969
申请日：2022-04-18
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Alexey Dosovitskiy , Ricardo Martin Brualla , Jonathan Tilton Barron , Noha Waheed Ahmed 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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公开(公告)号：US10187628B2
公开(公告)日：2019-01-22
申请号：US15676145
申请日：2017-08-14
Applicant: Google LLC
Inventor： Jonathan Tilton Barron , Stephen Joseph DiVerdi , Ryan Geiss
IPC: H04N13/239 , G06T7/38 , G06K9/62 , G06T5/00 , G06T5/10 , H04N9/09 , G06T7/269 , H04N5/235 , G06T7/292 , G06T7/30 , H04N13/271 , H04N13/00
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：US20240320912A1
公开(公告)日：2024-09-26
申请号：US18611236
申请日：2024-03-20
Applicant: Google LLC
Inventor： Yuanzhen Li , Amit Raj , Varun Jampani , Benjamin Joseph Mildenhall , Benjamin Michael Poole , Jonathan Tilton Barron , Kfir Aberman , Michael Niemeyer , Michael Rubinstein , Nataniel Ruiz Gutierrez , Shiran Elyahu Zada , Srinivas Kaza
IPC: G06T17/00 , H04N13/279 , H04N13/351
CPC classification number: G06T17/00 , H04N13/279 , H04N13/351
Abstract: A fractional training process can be performed training images to an instance of a machine-learned generative image model to obtain a partially trained instance of the model. A fractional optimization process can be performed with the partially trained instance to an instance of a machine-learned three-dimensional (3D) implicit representation model obtain a partially optimized instance of the model. Based on the plurality of training images, pseudo multi-view subject images can be generated with the partially optimized instance of the 3D implicit representation model and a fully trained instance of the generative image model; The partially trained instance of the model can be trained with a set of training data. The partially optimized instance of the machine-learned 3D implicit representation model can be trained with the machine-learned multi-view image model.
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公开(公告)号：US10897609B2
公开(公告)日：2021-01-19
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 , G06T7/38 , H04N5/235 , G06T5/00 , G06T7/30 , H04N13/271 , H04N13/25 , G06K9/62 , G06T5/10 , H04N9/09 , G06T7/269 , G06T7/292 , H04N13/00
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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Patent Agency Ranking
公开(公告)号：US20240420413A1
公开(公告)日：2024-12-19
申请号：US18813675
申请日：2024-08-23
Applicant: Google LLC
Inventor： Konstantinos Rematas , Thomas Allen Funkhouser , Vittorio Carlo Ferrari , Andrew Huaming Liu , Andrea Tagliasacchi , Pratul Preeti Srinivasan , Jonathan Tilton Barron
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：US20240273811A1
公开(公告)日：2024-08-15
申请号：US18012270
申请日：2022-10-24
Applicant: Google LLC
Inventor： Noha Radwan , Jonathan Tilton Barron , Benjamin Joseph Mildenhall , Seyed Mohammad Mehdi Sajjadi , Michael Niemeyer
CPC classification number: G06T15/205 , G06V10/82
Abstract: Systems and methods for training a neural radiance field model can include the use of image patches for ground truth training. For example, the systems and methods can include generating patch renderings with a neural radiance field model, comparing the patch renderings to ground truth patches from ground truth images, and adjusting one or more parameters based on the comparison. Additionally and/or alternatively, the systems and methods can include the utilization of a flow model for mitigating and/or minimizing artifact generation.
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公开(公告)号：US20240005590A1
公开(公告)日：2024-01-04
申请号：US18251995
申请日：2021-01-14
Applicant: GOOGLE LLC
Inventor： Ricardo Martin Brualla , Keunhong Park , Utkarsh Sinha , Sofien Bouaziz , Daniel Goldman , Jonathan Tilton Barron , Steven Maxwell Seitz
Abstract: Techniques of image synthesis using a neural radiance field (NeRF) includes generating a deformation model of movement experienced by a subject in a non-rigidly deforming scene. For example, when an image synthesis system uses NeRFs, the system takes as input multiple poses of subjects for training data. In contrast to conventional NeRFs, the technical solution first expresses the positions of the subjects from various perspectives in an observation frame. The technical solution then involves deriving a deformation model, i.e., a mapping between the observation frame and a canonical frame in which the subject's movements are taken into account. This mapping is accomplished using latent deformation codes for each pose that are determined using a multilayer perceptron (MLP). A NeRF is then derived from positions and casted ray directions in the canonical frame using another MLP. New poses for the subject may then be derived using the NeRF.
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公开(公告)号：US20230230275A1
公开(公告)日：2023-07-20
申请号：US18011601
申请日：2021-11-15
Applicant: Google LLC
Inventor： Tsung-Yi Lin , Peter Raymond Florence , Yen-Chen Lin , Jonathan Tilton Barron
IPC: G06T7/70
CPC classification number: G06T7/70 , G06T2207/20081 , G06T2207/20084 , G06T2207/30244
Abstract: Provided are systems and methods that invert a trained NeRF model, which stores the structure of a scene or object, to estimate the 6D pose from an image taken with a novel view. 6D pose estimation has a wide range of applications, including visual localization and object pose estimation for robot manipulation.
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公开(公告)号：US20220237834A1
公开(公告)日：2022-07-28
申请号：US17722969
申请日：2022-04-18
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Alexey Dosovitskiy , Ricardo Martin Brualla , Jonathan Tilton Barron , Noha Waheed Ahmed 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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