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公开(公告)号：US20250095275A1
公开(公告)日：2025-03-20
申请号：US18630480
申请日：2024-04-09
Applicant: NVIDIA Corporation
Inventor： Zian Wang , Tianchang Shen , Jun Gao , Merlin Nimier-David , Thomas Müller-Höhne , Alexander Keller , Sanja Fidler , Zan Gojcic , Nicholas Mark Worth Sharp
Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.
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公开(公告)号：US12243152B2
公开(公告)日：2025-03-04
申请号：US18441486
申请日：2024-02-14
Applicant: NVIDIA Corporation
Inventor： Wenzheng Chen , Joey Litalien , Jun Gao , Zian Wang , Clement Tse Tsian Christophe Louis Fuji Tsang , Sameh Khamis , Or Litany , Sanja Fidler
Abstract: In various examples, information may be received for a 3D model, such as 3D geometry information, lighting information, and material information. A machine learning model may be trained to disentangle the 3D geometry information, the lighting information, and/or material information from input data to provide the information, which may be used to project geometry of the 3D model onto an image plane to generate a mapping between pixels and portions of the 3D model. Rasterization may then use the mapping to determine which pixels are covered and in what manner, by the geometry. The mapping may also be used to compute radiance for points corresponding to the one or more 3D models using light transport simulation. Disclosed approaches may be used in various applications, such as image editing, 3D model editing, synthetic data generation, and/or data set augmentation.
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公开(公告)号：US20240312123A1
公开(公告)日：2024-09-19
申请号：US18592025
申请日：2024-02-29
Applicant: NVIDIA Corporation
Inventor： Malik Aqeel Anwar , Tae Eun Choe , Zian Wang , Sanja Fidler , Minwoo Park
IPC: G06T15/50 , G06T15/60 , G06T19/20 , G06V10/774 , H04N23/698
CPC classification number: G06T15/506 , G06T15/60 , G06T19/20 , H04N23/698 , G06T2219/2004 , G06V10/774
Abstract: In various examples, systems and methods are disclosed that relate to data augmentation for training/updating perception models in autonomous or semi-autonomous systems and applications. For example, a system may receive data associated with a set of frames that are captured using a plurality of cameras positioned in fixed relation relative to the machine; generate a panoramic view based at least on the set of frames; provide data associated with the panoramic view to a model to cause the model to generate a high dynamic range (HDR) panoramic view; determine lighting information associated with a light distribution map based at least on the HDR panoramic view; determine a virtual scene; and render an asset and a shadow on at least one of the frames, based at least on the virtual scene and the light distribution map, the shadow being a shadow corresponding to the asset.
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公开(公告)号：US11922558B2
公开(公告)日：2024-03-05
申请号：US17826611
申请日：2022-05-27
Applicant: NVIDIA Corporation
Inventor： Wenzheng Chen , Joey Litalien , Jun Gao , Zian Wang , Clement Tse Tsian Christophe Louis Fuji Tsang , Sameh Khamis , Or Litany , Sanja Fidler
CPC classification number: G06T15/06 , G06T15/506 , G06T19/20 , G06T2219/2012
Abstract: In various examples, information may be received for a 3D model, such as 3D geometry information, lighting information, and material information. A machine learning model may be trained to disentangle the 3D geometry information, the lighting information, and/or material information from input data to provide the information, which may be used to project geometry of the 3D model onto an image plane to generate a mapping between pixels and portions of the 3D model. Rasterization may then use the mapping to determine which pixels are covered and in what manner, by the geometry. The mapping may also be used to compute radiance for points corresponding to the one or more 3D models using light transport simulation. Disclosed approaches may be used in various applications, such as image editing, 3D model editing, synthetic data generation, and/or data set augmentation.
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Patent Agency Ranking
5.发明公开公开(公告)号：US20240054720A1
公开(公告)日：2024-02-15
申请号：US17886081
申请日：2022-08-11
Applicant: Nvidia Corporation
Inventor： Sanja Fidler , Zian Wang , Jan Kautz , Wenzheng Chen
CPC classification number: G06T15/506 , G06T5/009 , G06T7/586 , G06T2207/20081 , G06T2207/20208
Abstract: Systems and methods generate a hybrid lighting model for rendering objects within an image. The hybrid lighting model includes lighting effects attributed to a first source, such as the sun, and to a second source, such as spatially-varying effects of objects within the image. The hybrid lighting model may be generated for an input image and then one or more virtual objects may be rendered to appear as if part of the input image, where the hybrid lighting model is used to apply one or more lighting effects to the one or more virtual objects.
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6.发明公开公开(公告)号：US20240312122A1
公开(公告)日：2024-09-19
申请号：US18184459
申请日：2023-03-15
Applicant: Nvidia Corporation
Inventor： Nicolas Moenne-Loccoz , Zan Gojcic , Gavriel State , Zian Wang , Ignacio Llamas
CPC classification number: G06T15/506 , G06T5/50 , G06V10/60 , G06V10/761 , G06V10/82 , G06T2207/20221
Abstract: Approaches presented herein provide for the generation of visual content, including different types of content representations from different sources, rendered to include consistent scene illumination for the various representations. A first render pass can produce a first image including only proxies of implicit representations (e.g., NeRF objects) under scene illumination. A second render pass can produce a second image that includes a representation of the explicit scene objects, as well as the proxies of the implicit representations, under the scene illumination, which produces secondary lighting effects. The first and second images are compared to determine irradiance ratio data for the various pixel locations. A third render pass can produce a third image that includes the implicit representations, which can have relighting performed according to the irradiance ratio data to include the secondary lighting effects. The implicit and explicit objects can then be composited to produce an image with consistent scene illumination.
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7.发明公开公开(公告)号：US20240185506A1
公开(公告)日：2024-06-06
申请号：US18441486
申请日：2024-02-14
Applicant: NVIDIA Corporation
Inventor： Wenzheng Chen , Joey Litalien , Jun Gao , Zian Wang , Clement Tse Tsian Christophe Louis Fuji Tsang , Sameh Khamis , Or Litany , Sanja Fidler
CPC classification number: G06T15/06 , G06T15/506 , G06T19/20 , G06T2219/2012
Abstract: In various examples, information may be received for a 3D model, such as 3D geometry information, lighting information, and material information. A machine learning model may be trained to disentangle the 3D geometry information, the lighting information, and/or material information from input data to provide the information, which may be used to project geometry of the 3D model onto an image plane to generate a mapping between pixels and portions of the 3D model. Rasterization may then use the mapping to determine which pixels are covered and in what manner, by the geometry. The mapping may also be used to compute radiance for points corresponding to the one or more 3D models using light transport simulation. Disclosed approaches may be used in various applications, such as image editing, 3D model editing, synthetic data generation, and/or data set augmentation.
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公开(公告)号：US20240096017A1
公开(公告)日：2024-03-21
申请号：US17895793
申请日：2022-08-25
Applicant: Nvidia Corporation
Inventor： Jun Gao , Tianchang Shen , Zan Gojcic , Wenzheng Chen , Zian Wang , Daiqing Li , Or Litany , Sanja Fidler
IPC: G06T17/20
CPC classification number: G06T17/20 , G06T2207/10024 , G06T2207/20084
Abstract: Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate one or more textured three-dimensional meshes corresponding to one or more objects based, at least in part, one or more two-dimensional images of the one or more objects.
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公开(公告)号：US20220383582A1
公开(公告)日：2022-12-01
申请号：US17826611
申请日：2022-05-27
Applicant: NVIDIA Corporation
Inventor： Wenzheng Chen , Joey Litalien , Jun Gao , Zian Wang , Clement Tse Tsian Christophe Louis Fuji Tsang , Sameh Khamis , Or Litany , Sanja Fidler
Abstract: In various examples, information may be received for a 3D model, such as 3D geometry information, lighting information, and material information. A machine learning model may be trained to disentangle the 3D geometry information, the lighting information, and/or material information from input data to provide the information, which may be used to project geometry of the 3D model onto an image plane to generate a mapping between pixels and portions of the 3D model. Rasterization may then use the mapping to determine which pixels are covered and in what manner, by the geometry. The mapping may also be used to compute radiance for points corresponding to the one or more 3D models using light transport simulation. Disclosed approaches may be used in various applications, such as image editing, 3D model editing, synthetic data generation, and/or data set augmentation.
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