公开(公告)号：US20240251171A1

公开(公告)日：2024-07-25

申请号：US18406006

申请日：2024-01-05

Applicant: NVIDIA CORPORATION

Inventor： Iuri FROSIO ,  Yazhou XING ,  Chao LIU ,  Anjul PATNEY ,  Hongxu YIN ,  Amrita MAZUMDAR ,  Jan KAUTZ

IPC: H04N23/73 ,  H04N23/95

CPC classification number: H04N23/73 ,  H04N23/95

Abstract: One or more embodiments include receiving one or more frames of a live video captured by a video capturing device, wherein the one or more frames include a current frame that is most-recently captured, identifying a set of reference frames included in the one or more frames based on at least the current frame, wherein each frame in the set of reference frames has a different exposure level relative to the current frame, determining, using one or more neural networks, a set of missing details for one or more regions of the current frame based on the set of reference frames, generating an updated version of the current frame based on the set of details, and outputting the updated version of the current frame in real-time.

公开(公告)号：US20230267306A1

公开(公告)日：2023-08-24

申请号：US17933806

申请日：2022-09-20

Applicant: NVIDIA CORPORATION

Inventor： Benjamin ECKART ,  Jan KAUTZ ,  Chao LIU ,  Benjamin WU

IPC: G06N3/04 ,  G06T5/10

CPC classification number: G06N3/0454 ,  G06T5/10 ,  G06T2207/20056

Abstract: In various embodiments, a training application generates a trained machine learning model that represents items in a spectral domain. The training application executes a first neural network on a first set of data points associated with both a first item and the spectral domain to generate a second neural network. Subsequently, the training application generates a set of predicted data points that are associated with both the first item and the spectral domain via the second neural network. The training application generates the trained machine learning model based on the first neural network, the second neural network, and the set of predicted data points. The trained machine learning model maps one or more positions within the spectral domain to one or more values associated with an item based on a set of data points associated with both the item and the spectral domain.

公开(公告)号：US20240303840A1

公开(公告)日：2024-09-12

申请号：US18508139

申请日：2023-11-13

Applicant: NVIDIA CORPORATION

Inventor： Chao LIU ,  Benjamin ECKART ,  Jan KAUTZ

IPC: G06T7/50 ,  G06T7/20 ,  G06V10/762

CPC classification number: G06T7/50 ,  G06T7/20 ,  G06V10/762

Abstract: The disclosed method for generating a first depth map for a first frame of a video includes performing one or more operations to generate a first intermediate depth map based on the first frame and a second frame preceding the first frame within the video, performing one or more operations to generate a second intermediate depth map based on the first frame, and performing one or more operations to combine the first intermediate depth map and the second intermediate depth map to generate the first depth map.

公开(公告)号：US20230368468A1

公开(公告)日：2023-11-16

申请号：US17744467

申请日：2022-05-13

Applicant: NVIDIA CORPORATION

Inventor： Ben ECKART ,  Christopher CHOY ,  Chao LIU ,  Yurong YOU

IPC: G06T17/10 ,  G06N3/04 ,  G06N3/08 ,  G06T19/20

CPC classification number: G06T17/10 ,  G06N3/0454 ,  G06N3/084 ,  G06T19/20 ,  G06T2219/2016

Abstract: In various embodiments, an unsupervised training application executes a neural network on a first point cloud to generate keys and values. The unsupervised training application generates output vectors based on a first query set, the keys, and the values and then computes spatial features based on the output vectors. The unsupervised training application computes quantized context features based on the output vectors and a first set of codes representing a first set of 3D geometry blocks. The unsupervised training application modifies the first neural network based on a likelihood of reconstructing the first point cloud, the quantized context features, and the spatial features to generate an updated neural network. A trained machine learning model includes the updated neural network, a second query set, and a second set of codes representing a second set of 3D geometry blocks and maps a point cloud to a representation of 3D geometry instances.

公开(公告)号：US20230368032A1

公开(公告)日：2023-11-16

申请号：US17744456

申请日：2022-05-13

Applicant: NVIDIA CORPORATION

Inventor： Ben ECKART ,  Christopher CHOY ,  Chao LIU ,  Yurong YOU

IPC: G06N3/08 ,  G06T17/10

CPC classification number: G06N3/084 ,  G06T17/10

Abstract: In various embodiments, an unsupervised training application trains a machine learning model to generate representations of point clouds. The unsupervised training application executes a neural network on a first point cloud representing a first three-dimensional (3D) scene to generate segmentations. Based on the segmentations, the unsupervised training application computes spatial features. The unsupervised training application computes quantized context features based on the segmentations and a first set of codes representing a first set of 3D geometry blocks. The unsupervised training application modifies the neural network based on a likelihood of reconstructing the first point cloud, the quantized context features, and the spatial features to generate an updated neural network. A trained machine learning model that includes the updated neural network and a second set of codes representing a second set of 3D geometry blocks maps a point cloud representing a 3D scene to a representation of 3D geometry instances.

公开(公告)号：US20230267659A1

公开(公告)日：2023-08-24

申请号：US17933811

申请日：2022-09-20

Applicant: NVIDIA CORPORATION

Inventor： Benjamin ECKART ,  Jan KAUTZ ,  Chao LIU ,  Benjamin WU

IPC: G06T11/00 ,  G06F17/14 ,  G01B9/02

CPC classification number: G06T11/006 ,  G06F17/141 ,  G01B9/02041

Abstract: In various embodiments, an inference application reconstructs representations of items in a spectral domain. The inference application maps a first set of data points associated with a both an item and the spectral domain to conditioning information via a first trained machine learning model. The inference application updates a second trained machine learning model based on the conditioning information to generate a model that represents the item within the spectral domain. The inference application generates a second set of data points associated with both the item and the spectral domain via the model. The inference application constructs an image associated with the item based on the second set of data points.

公开(公告)号：US20230267656A1

公开(公告)日：2023-08-24

申请号：US17933813

申请日：2022-09-20

Applicant: NVIDIA CORPORATION

Inventor： Benjamin ECKART ,  Jan KAUTZ ,  Chao LIU ,  Benjamin WU

IPC: G06T11/00 ,  G06T7/00

CPC classification number: G06T11/003 ,  G06T7/0012 ,  G06T2207/20056 ,  G06T2207/20081

Abstract: In various embodiments, an inference application constructs medical images. The inference application executes a first trained machine learning model on a set of data points associated with a both a medical item and a spectral domain to generate a second model that represents the medical item within the spectral domain. The inference application maps a set of positions to a set of predicted values associated with both the medical item and the spectral domain via the second model. The inference application constructs an image of the medical item based on the first set of predicted values.