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公开(公告)号：US20200294194A1
公开(公告)日：2020-09-17
申请号：US16299062
申请日：2019-03-11
Applicant: NVIDIA Corporation
Inventor： Deqing Sun , Orazio Gallo , Jan Kautz , Jinwei GU , Wei-Sheng Lai
Abstract: A video stitching system combines video from different cameras to form a panoramic video that, in various embodiments, is temporally stable and tolerant to strong parallax. In an embodiment, the system provides a smooth spatial interpolation that can be used to connect the input video images. In an embodiment, the system applies an interpolation layer to slices of the overlapping video sources, and the network learns a dense flow field to smoothly align the input videos with spatial interpolation. Various embodiments are applicable to areas such as virtual reality, immersive telepresence, autonomous driving, and video surveillance.
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公开(公告)号：US10762425B2
公开(公告)日：2020-09-01
申请号：US16134716
申请日：2018-09-18
Applicant: NVIDIA Corporation
Inventor： Sifei Liu , Shalini De Mello , Jinwei Gu , Ming-Hsuan Yang , Jan Kautz
Abstract: A spatial linear propagation network (SLPN) system learns the affinity matrix for vision tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The SLPN system is trained for a particular computer vision task and refines an input map (i.e., affinity matrix) that indicates pixels the share a particular property (e.g., color, object, texture, shape, etc.). Inputs to the SLPN system are input data (e.g., pixel values for an image) and the input map corresponding to the input data to be propagated. The input data is processed to produce task-specific affinity values (guidance data). The task-specific affinity values are applied to values in the input map, with at least two weighted values from each column contributing to a value in the refined map data for the adjacent column.
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公开(公告)号：US20200273207A1
公开(公告)日：2020-08-27
申请号：US16872752
申请日：2020-05-12
Applicant: NVIDIA Corporation
Inventor： Jinwei Gu , Samarth Manoj Brahmbhatt , Kihwan Kim , Jan Kautz
Abstract: A deep neural network (DNN) system learns a map representation for estimating a camera position and orientation (pose). The DNN is trained to learn a map representation corresponding to the environment, defining positions and attributes of structures, trees, walls, vehicles, etc. The DNN system learns a map representation that is versatile and performs well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i.e., recover the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robot navigation, car localization for autonomous driving, device localization for mobile navigation, and augmented/virtual reality.
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公开(公告)号：US10595039B2
公开(公告)日：2020-03-17
申请号：US15939098
申请日：2018-03-28
Applicant: NVIDIA Corporation
Inventor： Ming-Yu Liu , Xiaodong Yang , Jan Kautz , Sergey Tulyakov
IPC: H04N19/513 , G06K9/00 , G06N3/08 , G06T13/40 , G06N3/04
Abstract: A method, computer readable medium, and system are disclosed for action video generation. The method includes the steps of generating, by a recurrent neural network, a sequence of motion vectors from a first set of random variables and receiving, by a generator neural network, the sequence of motion vectors and a content vector sample. The sequence of motion vectors and the content vector sample are sampled by the generator neural network to produce a video clip.
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公开(公告)号：US20190228495A1
公开(公告)日：2019-07-25
申请号：US16255038
申请日：2019-01-23
Applicant: Nvidia Corporation
Inventor： Jonathan Tremblay , Stan Birchfield , Stephen Tyree , Thang To , Jan Kautz , Artem Molchanov
Abstract: Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.
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Patent Agency Ranking
公开(公告)号：US20190163978A1
公开(公告)日：2019-05-30
申请号：US16202703
申请日：2018-11-28
Applicant: NVIDIA Corporation
Inventor： Xiaodong Yang , Pavlo Molchanov , Jan Kautz , Behrooz Mahasseni
Abstract: Detection of activity in video content, and more particularly detecting in video start and end frames inclusive of an activity and a classification for the activity, is fundamental for video analytics including categorizing, searching, indexing, segmentation, and retrieval of videos. Existing activity detection processes rely on a large set of features and classifiers that exhaustively run over every time step of a video at multiple temporal scales, or as a small improvement computationally propose segments of the video on which to perform classification. These existing activity detection processes, however, are computationally expensive, particularly when trying to achieve activity detection accuracy, and moreover are not configurable for any particular time or computation budget. The present disclosure provides a time and/or computation budget-aware method for detecting activity in video that relies on a recurrent neural network implementing a learned policy.
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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：US20180365532A1
公开(公告)日：2018-12-20
申请号：US16006709
申请日：2018-06-12
Applicant: NVIDIA Corporation
Inventor： Pavlo Molchanov , Stephen Walter Tyree , Jan Kautz , Sina Honari
Abstract: A method, computer readable medium, and system are disclosed for sequential multi-tasking to generate coordinates of landmarks within images. The landmark locations may be identified on an image of a human face and used for emotion recognition, face identity verification, eye gaze tracking, pose estimation, etc. A neural network model processes input image data to generate pixel-level likelihood estimates for landmarks in the input image data and a soft-argmax function computes predicted coordinates of each landmark based on the pixel-level likelihood estimates.
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公开(公告)号：US20180365512A1
公开(公告)日：2018-12-20
申请号：US16006728
申请日：2018-06-12
Applicant: NVIDIA Corporation
Inventor： Pavlo Molchanov , Stephen Walter Tyree , Jan Kautz , Sina Honari
Abstract: A method, computer readable medium, and system are disclosed to generate coordinates of landmarks within images. The landmark locations may be identified on an image of a human face and used for emotion recognition, face identity verification, eye gaze tracking, pose estimation, etc. A transform is applied to input image data to produce transformed input image data. The transform is also applied to predicted coordinates for landmarks of the input image data to produce transformed predicted coordinates. A neural network model processes the transformed input image data to generate additional landmarks of the transformed input image data and additional predicted coordinates for each one of the additional landmarks. Parameters of the neural network model are updated to reduce differences between the transformed predicted coordinates and the additional predicted coordinates.
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公开(公告)号：US09830703B2
公开(公告)日：2017-11-28
申请号：US14825129
申请日：2015-08-12
Applicant: NVIDIA CORPORATION
Inventor： Gregory P. Meyer , Shalini Gupta , Iuri Frosio , Nagilla Dikpal Reddy , Jan Kautz
CPC classification number: G06T7/507 , G06K9/6276 , G06T7/251 , G06T7/277 , G06T7/70 , G06T7/75 , G06T7/77 , G06T2200/28 , G06T2207/10016 , G06T2207/10028 , G06T2207/30201
Abstract: One embodiment of the present invention sets forth a technique for estimating a head pose of a user. The technique includes acquiring depth data associated with a head of the user and initializing each particle included in a set of particles with a different candidate head pose. The technique further includes performing one or more optimization passes that include performing at least one iterative closest point (ICP) iteration for each particle and performing at least one particle swarm optimization (PSO) iteration. Each ICP iteration includes rendering the three-dimensional reference model based on the candidate head pose associated with the particle and comparing the three-dimensional reference model to the depth data. Each PSO iteration comprises updating a global best head pose associated with the set of particles and modifying at least one candidate head pose. The technique further includes modifying a shape of the three-dimensional reference model based on depth data.
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