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1.发明公开公开(公告)号：US20240362935A1
公开(公告)日：2024-10-31
申请号：US18305185
申请日：2023-04-21
Applicant: NVIDIA Corporation
Inventor： Anton Mitrokhin , Roman Parys , Alexey Solovey , Tilman Wekel
Abstract: In various examples, generating maps using first sensor data and then annotating second sensor data using the maps for autonomous systems and applications is described herein. Systems and methods are disclosed that automatically propagate annotations associated with the first sensor data generated using a first type of sensor, such as a LiDAR sensor, to the second sensor data generated using a second type of sensor, such as an image sensor(s). To propagate the annotations, the first type of sensor data may be used to generate a map, where the map represents the locations of static objects as well as the locations of dynamic objects at various instances in time. The map and annotations associated with the first sensor data may then be used to annotate the second sensor data and/or determine additional information associated with the objects represented by the second sensors data.
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公开(公告)号：US11341710B2
公开(公告)日：2022-05-24
申请号：US16766166
申请日：2019-11-21
Applicant: Nvidia Corporation
Inventor： Evgenii Tumanov , Dmitry Korobchenko , Alexey Solovey
Abstract: Approaches in accordance with various embodiments provide for fluid simulation with substantially reduced time and memory requirements with respect to conventional approaches. In particular, various embodiments can perform time and energy efficient, large scale fluid simulation on processing hardware using a method that does not solve for the Navier-Stokes equations to enforce incompressibility. Instead, various embodiments generate a density tensor and rigid body map tensor for a large number of particles contained in a sub-domain. Collectively, the density tensor and rigid body map may represent input channels of a network with three spatial-dimensions. The network may apply a series of operations to the input channels to predict an updated position and updated velocity for each particle at the end of a frame. Such approaches can handle tens of millions of particles within a virtually unbounded simulation domain, as compared to classical approaches that solve for the Navier-Stokes equations.
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公开(公告)号：US20200269136A1
公开(公告)日：2020-08-27
申请号：US16287670
申请日：2019-02-27
Applicant: Nvidia Corporation
Inventor： Prakash Gurumurthy , Yan Breek , Alexey Solovey , Evgeny Tumanov
Abstract: Personalized coaching is provided to users of an application, such as players of an electronic gaming application. Data can be obtained that demonstrates how skilled users utilize an application, such as how professional players play a game. This data can be used to train a machine learning model for the game. Gameplay data for an identified player can be obtained, and related information provided as input to the trained model. The model can infer one or more actions or strategies to be taken by the player in order to achieve a determined goal. The information can be conveyed to the player using visual, audio, or haptic guidance during gameplay, or can be provided offline, such as with video or rendered replay of the game session. The types of advice or coaching given can vary depending upon factors such as the goals, skill level, and preferences of the player, and can update over time.
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公开(公告)号：US20220274018A1
公开(公告)日：2022-09-01
申请号：US17748975
申请日：2022-05-19
Applicant: Nvidia Corporation
Inventor： Prakash Gurumurthy , Yan Breek , Alexey Solovey , Evgeny Tumanov
Abstract: Personalized coaching is provided to users of an application, such as players of an electronic gaming application. Data can be obtained that demonstrates how skilled users utilize an application, such as how professional players play a game. This data can be used to train a machine learning model for the game. Gameplay data for an identified player can be obtained, and related information provided as input to the trained model. The model can infer one or more actions or strategies to be taken by the player in order to achieve a determined goal. The information can be conveyed to the player using visual, audio, or haptic guidance during gameplay, or can be provided offline, such as with video or rendered replay of the game session. The types of advice or coaching given can vary depending upon factors such as the goals, skill level, and preferences of the player, and can update over time.
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Patent Agency Ranking
公开(公告)号：US11376500B2
公开(公告)日：2022-07-05
申请号：US16287670
申请日：2019-02-27
Applicant: Nvidia Corporation
Inventor： Prakash Gurumurthy , Yan Breek , Alexey Solovey , Evgeny Tumanov
Abstract: Personalized coaching is provided to users of an application, such as players of an electronic gaming application. Data can be obtained that demonstrates how skilled users utilize an application, such as how professional players play a game. This data can be used to train a machine learning model for the game. Gameplay data for an identified player can be obtained, and related information provided as input to the trained model. The model can infer one or more actions or strategies to be taken by the player in order to achieve a determined goal. The information can be conveyed to the player using visual, audio, or haptic guidance during gameplay, or can be provided offline, such as with video or rendered replay of the game session. The types of advice or coaching given can vary depending upon factors such as the goals, skill level, and preferences of the player, and can update over time.
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公开(公告)号：US20210158603A1
公开(公告)日：2021-05-27
申请号：US16766166
申请日：2019-11-21
Applicant: Nvidia Corporation
Inventor： Evegny Tumanov , Dmitry Korobchenko , Alexey Solovey
Abstract: Approaches in accordance with various embodiments provide for fluid simulation with substantially reduced time and memory requirements with respect to conventional approaches. In particular, various embodiments can perform time and energy efficient, large scale fluid simulation on processing hardware using a method that does not solve for the Navier-Stokes equations to enforce incompressibility. Instead, various embodiments generate a density tensor and rigid body map tensor for a large number of particles contained in a sub-domain. Collectively, the density tensor and rigid body map may represent input channels of a network with three spatial-dimensions. The network may apply a series of operations to the input channels to predict an updated position and updated velocity for each particle at the end of a frame. Such approaches can handle tens of millions of particles within a virtually unbounded simulation domain, as compared to classical approaches that solve for the Navier-Stokes equations.
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公开(公告)号：US20240353234A1
公开(公告)日：2024-10-24
申请号：US18305153
申请日：2023-04-21
Applicant: NVIDIA Corporation
Inventor： Anton Mitrokhin , Roman Parys , Alexey Solovey , Tilman Wekel
CPC classification number: G01C21/3804 , G06T17/00 , G06V10/761 , G06V2201/07
Abstract: In various examples, generating maps using first sensor data and then annotating second sensor data using the maps for autonomous systems and applications is described herein. Systems and methods are disclosed that automatically propagate annotations associated with the first sensor data generated using a first type of sensor, such as a LiDAR sensor, to the second sensor data generated using a second type of sensor, such as an image sensor(s). To propagate the annotations, the first type of sensor data may be used to generate a map, where the map represents the locations of static objects as well as the locations of dynamic objects at various instances in time. The map and annotations associated with the first sensor data may then be used to annotate the second sensor data and/or determine additional information associated with the objects represented by the second sensors data.
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公开(公告)号：US11954791B2
公开(公告)日：2024-04-09
申请号：US17751360
申请日：2022-05-23
Applicant: Nvidia Corporation
Inventor： Evgenii Tumanov , Dmitry Korobchenko , Alexey Solovey
Abstract: Approaches in accordance with various embodiments provide for fluid simulation with substantially reduced time and memory requirements with respect to conventional approaches. In particular, various embodiments can perform time and energy efficient, large scale fluid simulation on processing hardware using a method that does not solve for the Navier-Stokes equations to enforce incompressibility. Instead, various embodiments generate a density tensor and rigid body map tensor for a large number of particles contained in a sub-domain. Collectively, the density tensor and rigid body map may represent input channels of a network with three spatial-dimensions. The network may apply a series of operations to the input channels to predict an updated position and updated velocity for each particle at the end of a frame. Such approaches can handle tens of millions of particles within a virtually unbounded simulation domain, as compared to classical approaches that solve for the Navier-Stokes equations.
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公开(公告)号：US20220358710A1
公开(公告)日：2022-11-10
申请号：US17751360
申请日：2022-05-23
Applicant: Nvidia Corporation
Inventor： Evgenii Tumanov , Dmitry Korobchenko , Alexey Solovey
Abstract: Approaches in accordance with various embodiments provide for fluid simulation with substantially reduced time and memory requirements with respect to conventional approaches. In particular, various embodiments can perform time and energy efficient, large scale fluid simulation on processing hardware using a method that does not solve for the Navier-Stokes equations to enforce incompressibility. Instead, various embodiments generate a density tensor and rigid body map tensor for a large number of particles contained in a sub-domain. Collectively, the density tensor and rigid body map may represent input channels of a network with three spatial-dimensions. The network may apply a series of operations to the input channels to predict an updated position and updated velocity for each particle at the end of a frame. Such approaches can handle tens of millions of particles within a virtually unbounded simulation domain, as compared to classical approaches that solve for the Navier-Stokes equations.
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