专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

21. 发明申请

US20160071310A1 BEAM TRACING 有权
标题翻译：光束追踪
公开(公告)号：US20160071310A1
公开(公告)日：2016-03-10
申请号：US14662090
申请日：2015-03-18
申请人： NVIDIA Corporation
发明人： Tero Tapani Karras , Timo Oskari Aila , Samuli Matias Laine , John Erik Lindholm
IPC分类号： G06T15/06 , G06T15/80 , G06T15/00
CPC分类号： G06F17/30961 , G06F17/30327 , G06F17/30563 , G06T1/60 , G06T9/40 , G06T15/005 , G06T15/06 , G06T15/08 , G06T15/10 , G06T15/80 , G06T17/10 , H04N19/174 , H04N19/44
摘要： An apparatus, computer readable medium, and method are disclosed for performing an intersection query between a query beam and a target bounding volume. The target bounding volume may comprise an axis-aligned bounding box (AABB) associated with a bounding volume hierarchy (BVH) tree. An intersection query comprising beam information associated with the query beam and slab boundary information for a first dimension of a target bounding volume is received. Intersection parameter values are calculated for the first dimension based on the beam information and the slab boundary information and a slab intersection case is determined for the first dimension based on the beam information. A parametric variable range for the first dimension is assigned based on the slab intersection case and the intersection parameter values and it is determined whether the query beam intersects the target bounding volume based on at least the parametric variable range for the first dimension.
摘要翻译：公开了一种用于执行查询光束和目标界限卷之间的交叉查询的装置，计算机可读介质和方法。目标边界体积可以包括与边界体积分层（BVH）树相关联的轴对齐边界框（AABB）。接收包括与查询波束相关联的波束信息和用于目标边界体积的第一维度的平板边界信息的交叉点查询。基于波束信息和平板边界信息，针对第一维度计算交点参数值，并且基于波束信息为第一维度确定板交集情况。基于板交集案例和交集参数值分配第一维度的参数变量范围，并且基于至少第一维度的参数可变范围确定查询梁是否与目标界限卷相交。

22. 发明授权

US09245363B2 System, method, and computer program product implementing an algorithm for performing thin voxelization of a three-dimensional model 有权
标题翻译：实现用于执行三维模型的薄体素化的算法的系统，方法和计算机程序产品
公开(公告)号：US09245363B2
公开(公告)日：2016-01-26
申请号：US13725015
申请日：2012-12-21
申请人： NVIDIA Corporation
发明人： Samuli Matias Laine
IPC分类号： G06T11/40 , G06T17/00 , G06T15/08
CPC分类号： G06T11/40 , G06T15/08 , G06T17/00
摘要： A system, method, and computer program product for implementing an algorithm for performing thin voxelization is disclosed. The thin voxelization algorithm receives a surface, maps the surface onto a plurality of volumetric picture elements (voxels), and generates a value for each voxel in the plurality of voxels that intersects with the surface. A voxel intersects with the surface when the surface intersects a crosshair shape associated with the voxel.
摘要翻译：公开了一种用于实现用于执行薄体素化的算法的系统，方法和计算机程序产品。薄体素化算法接收表面，将表面映射到多个体积像素（体素）上，并且生成与表面相交的多个体素中的每个体素的值。当表面与与体素相关联的十字线形状相交时，体素与表面相交。

23. 发明授权

US11790598B2 Three-dimensional tomography reconstruction pipeline 有权
公开(公告)号：US11790598B2
公开(公告)日：2023-10-17
申请号：US17365574
申请日：2021-07-01
申请人： NVIDIA Corporation
发明人： Onni August Kosomaa , Jaakko T. Lehtinen , Samuli Matias Laine , Tero Tapani Karras , Miika Samuli Aittala
IPC分类号： G06T15/08 , G06T11/00 , G16H30/40 , G06N3/045
CPC分类号： G06T15/08 , G06N3/045 , G06T11/006 , G16H30/40 , G06T2210/41
摘要： A three-dimensional (3D) density volume of an object is constructed from tomography images (e.g., x-ray images) of the object. The tomography images are projection images that capture all structures of an object (e.g., human body) between a beam source and imaging sensor. The beam effectively integrates along a path through the object producing a tomography image at the imaging sensor, where each pixel represents attenuation. A 3D reconstruction pipeline includes a first neural network model, a fixed function backprojection unit, and a second neural network model. Given information for the capture environment, the tomography images are processed by the reconstruction pipeline to produce a reconstructed 3D density volume of the object. In contrast with a set of 2D slices, the entire 3D density volume is reconstructed, so two-dimensional (2D) density images may be produced by slicing through any portion of the 3D density volume at any angle.

24. 发明授权

US11763168B2 Progressive modification of generative adversarial neural networks 有权
公开(公告)号：US11763168B2
公开(公告)日：2023-09-19
申请号：US17567542
申请日：2022-01-03
申请人： NVIDIA Corporation
发明人： Tero Tapani Karras , Timo Oskari Aila , Samuli Matias Laine , Jaakko T. Lehtinen
IPC分类号： G06N3/04 , G06N3/08 , G06N3/088 , G06N3/082 , G06N3/0475
CPC分类号： G06N3/088 , G06N3/04 , G06N3/0475 , G06N3/082
摘要： A generative adversarial neural network (GAN) learns a particular task by being shown many examples. In one scenario, a GAN may be trained to generate new images including specific objects, such as human faces, bicycles, etc. Rather than training a complex GAN having a predetermined topology of features and interconnections between the features to learn the task, the topology of the GAN is modified as the GAN is trained for the task. The topology of the GAN may be simple in the beginning and become more complex as the GAN learns during the training, eventually evolving to match the predetermined topology of the complex GAN. In the beginning the GAN learns large-scale details for the task (bicycles have two wheels) and later, as the GAN becomes more complex, learns smaller details (the wheels have spokes).

25. 发明授权

US11682199B2 Style-based architecture for generative neural networks 有权
公开(公告)号：US11682199B2
公开(公告)日：2023-06-20
申请号：US17821686
申请日：2022-08-23
申请人： NVIDIA Corporation
发明人： Tero Tapani Karras , Timo Oskari Aila , Samuli Matias Laine
IPC分类号： G06N3/04 , G06V10/82 , G06N3/088 , G06N3/045 , G06N3/08
CPC分类号： G06V10/82 , G06N3/045 , G06N3/088 , G06N3/08
摘要： A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people's faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.

26. 发明公开

US20230186617A1 STYLE-BASED ARCHITECTURE FOR GENERATIVE NEURAL NETWORKS 审中-公开
公开(公告)号：US20230186617A1
公开(公告)日：2023-06-15
申请号：US18157375
申请日：2023-01-20
申请人： NVIDIA Corporation
发明人： Tero Tapani Karras , Timo Oskari Aila , Samuli Matias Laine
IPC分类号： G06V10/82 , G06N3/088 , G06N3/045
CPC分类号： G06V10/82 , G06N3/045 , G06N3/088 , G06N3/08
摘要： A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people’s faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.

27. 发明授权

US11610122B2 Generative adversarial neural network assisted reconstruction 有权
公开(公告)号：US11610122B2
公开(公告)日：2023-03-21
申请号：US17143608
申请日：2021-01-07
申请人： NVIDIA Corporation
发明人： Tero Tapani Karras , Samuli Matias Laine , David Patrick Luebke , Jaakko T. Lehtinen , Miika Samuli Aittala , Timo Oskari Aila , Ming-Yu Liu , Arun Mohanray Mallya , Ting-Chun Wang
IPC分类号： G06T9/00 , G06N3/08 , H04N19/20 , H04N7/15 , G06T7/73 , G06V40/16 , G06T5/00
摘要： A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.

28. 发明授权

US11580395B2 Generative adversarial neural network assisted video reconstruction 有权
公开(公告)号：US11580395B2
公开(公告)日：2023-02-14
申请号：US17069449
申请日：2020-10-13
申请人： NVIDIA Corporation
发明人： Tero Tapani Karras , Samuli Matias Laine , David Patrick Luebke , Jaakko T. Lehtinen , Miika Samuli Aittala , Timo Oskari Aila , Ming-Yu Liu , Arun Mohanray Mallya , Ting-Chun Wang
IPC分类号： G06T9/00 , G06N3/08 , H04N19/20 , H04N7/15 , G06T7/73 , G06V40/16 , G06T5/00
摘要： A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.

29. 发明申请

US20220406048A1 STYLE-BASED ARCHITECTURE FOR GENERATIVE NEURAL NETWORKS 有权
公开(公告)号：US20220406048A1
公开(公告)日：2022-12-22
申请号：US17821686
申请日：2022-08-23
申请人： NVIDIA Corporation
发明人： Tero Tapani Karras , Timo Oskari Aila , Samuli Matias Laine
IPC分类号： G06V10/82 , G06N3/04 , G06N3/08
摘要： A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people's faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.

30. 发明申请

US20220398004A1 User Interfaces and Methods for Generating a New Artifact Based on Existing Artifacts 有权
公开(公告)号：US20220398004A1
公开(公告)日：2022-12-15
申请号：US17344053
申请日：2021-06-10
申请人： NVIDIA Corporation
发明人： Janne Hellsten , Tero Tapani Karras , Samuli Matias Laine
IPC分类号： G06F3/0484 , G06T11/40 , G06T11/60 , G06N3/04 , G06F3/0481
摘要： User interfaces, methods and structures are described for intuitively and fluidly creating new artifacts from existing artifacts and for exploring latent spaces in a visual manner. In example embodiments, source artifacts are displayed along with a selector. The selector is operable to indicate a selected set of the source artifacts by establishing a selection region that includes portions of one or more of the source artifacts displayed. Source vectors are associated with the source artifacts in the selected set. One or more resultant vectors are determined based on the source vectors, and an output artifact is generated based on the one or more resultant vectors.

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式