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公开(公告)号：US10929987B2
公开(公告)日：2021-02-23
申请号：US16052528
申请日：2018-08-01
Applicant: NVIDIA Corporation
Inventor： Zhaoyang Lv , Kihwan Kim , Deqing Sun , Alejandro Jose Troccoli , Jan Kautz
IPC: G06T7/254 , G06T7/90 , G06T7/50 , G06N3/08 , G06T7/194 , G06T3/00 , G06T7/70 , G06T7/60 , G06T7/11 , G06N5/04 , G06T7/285 , G06T7/215
Abstract: A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.
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公开(公告)号：US10482196B2
公开(公告)日：2019-11-19
申请号：US15055440
申请日：2016-02-26
Applicant: NVIDIA Corporation
Inventor： Benjamin David Eckart , Kihwan Kim , Alejandro Jose Troccoli , Jan Kautz
Abstract: A method, computer readable medium, and system are disclosed for generating a Gaussian mixture model hierarchy. The method includes the steps of receiving point cloud data defining a plurality of points; defining a Gaussian Mixture Model (GMM) hierarchy that includes a number of mixels, each mixel encoding parameters for a probabilistic occupancy map; and adjusting the parameters for one or more probabilistic occupancy maps based on the point cloud data utilizing a number of iterations of an Expectation-Maximum (EM) algorithm.
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公开(公告)号：US20190355103A1
公开(公告)日：2019-11-21
申请号：US16353195
申请日：2019-03-14
Applicant: NVIDIA Corporation
Inventor： Seung-Hwan Baek , Kihwan Kim , Jinwei Gu , Orazio Gallo , Alejandro Jose Troccoli , Ming-Yu Liu , Jan Kautz
Abstract: Missing image content is generated using a neural network. In an embodiment, a high resolution image and associated high resolution semantic label map are generated from a low resolution image and associated low resolution semantic label map. The input image/map pair (low resolution image and associated low resolution semantic label map) lacks detail and is therefore missing content. Rather than simply enhancing the input image/map pair, data missing in the input image/map pair is improvised or hallucinated by a neural network, creating plausible content while maintaining spatio-temporal consistency. Missing content is hallucinated to generate a detailed zoomed in portion of an image. Missing content is hallucinated to generate different variations of an image, such as different seasons or weather conditions for a driving video.
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Patent Agency Ranking
公开(公告)号：US20170249401A1
公开(公告)日：2017-08-31
申请号：US15055440
申请日：2016-02-26
Applicant: NVIDIA Corporation
Inventor： Benjamin David Eckart , Kihwan Kim , Alejandro Jose Troccoli , Jan Kautz
CPC classification number: G06F17/5009 , G06F17/18 , G06F2217/16 , G06K9/00986 , G06K9/6219 , G06K9/6277 , G06K9/6282 , G06N5/003 , G06N7/005
Abstract: A method, computer readable medium, and system are disclosed for generating a Gaussian mixture model hierarchy. The method includes the steps of receiving point cloud data defining a plurality of points; defining a Gaussian Mixture Model (GMM) hierarchy that includes a number of mixels, each mixel encoding parameters for a probabilistic occupancy map; and adjusting the parameters for one or more probabilistic occupancy maps based on the point cloud data utilizing a number of iterations of an Expectation-Maximum (EM) algorithm.
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公开(公告)号：US20210150736A1
公开(公告)日：2021-05-20
申请号：US17156406
申请日：2021-01-22
Applicant: NVIDIA Corporation
Inventor： Zhaoyang Lv , Kihwan Kim , Deqing Sun , Alejandro Jose Troccoli , Jan Kautz
IPC: G06T7/254 , G06T7/90 , G06T7/50 , G06N3/08 , G06T7/194 , G06T3/00 , G06T7/70 , G06T7/60 , G06T7/11 , G06N5/04 , G06T7/285 , G06T7/215
Abstract: A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.
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公开(公告)号：US20190057509A1
公开(公告)日：2019-02-21
申请号：US16052528
申请日：2018-08-01
Applicant: NVIDIA Corporation
Inventor： Zhaoyang Lv , Kihwan Kim , Deqing Sun , Alejandro Jose Troccoli , Jan Kautz
IPC: G06T7/254 , G06T7/90 , G06T7/50 , G06N3/08 , G06N5/04 , G06T3/00 , G06T7/70 , G06T7/60 , G06T7/11 , G06T7/194
Abstract: A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.
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公开(公告)号：US11508076B2
公开(公告)日：2022-11-22
申请号：US17156406
申请日：2021-01-22
Applicant: NVIDIA Corporation
Inventor： Zhaoyang Lv , Kihwan Kim , Deqing Sun , Alejandro Jose Troccoli , Jan Kautz
IPC: G06T7/254 , G06T7/90 , G06T7/50 , G06N3/08 , G06T7/194 , G06T3/00 , G06T7/70 , G06T7/60 , G06T7/11 , G06N5/04 , G06T7/285 , G06T7/215
Abstract: A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.
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公开(公告)号：US10922793B2
公开(公告)日：2021-02-16
申请号：US16353195
申请日：2019-03-14
Applicant: NVIDIA Corporation
Inventor： Seung-Hwan Baek , Kihwan Kim , Jinwei Gu , Orazio Gallo , Alejandro Jose Troccoli , Ming-Yu Liu , Jan Kautz
Abstract: Missing image content is generated using a neural network. In an embodiment, a high resolution image and associated high resolution semantic label map are generated from a low resolution image and associated low resolution semantic label map. The input image/map pair (low resolution image and associated low resolution semantic label map) lacks detail and is therefore missing content. Rather than simply enhancing the input image/map pair, data missing in the input image/map pair is improvised or hallucinated by a neural network, creating plausible content while maintaining spatio-temporal consistency. Missing content is hallucinated to generate a detailed zoomed in portion of an image. Missing content is hallucinated to generate different variations of an image, such as different seasons or weather conditions for a driving video.
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