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公开(公告)号：US12183037B2
公开(公告)日：2024-12-31
申请号：US18487800
申请日：2023-10-16
Applicant: NVIDIA Corporation
Inventor： Sravya Nimmagadda , David Weikersdorfer
Abstract: An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.
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公开(公告)号：US20240176018A1
公开(公告)日：2024-05-30
申请号：US18060444
申请日：2022-11-30
Applicant: NVIDIA Corporation
Inventor： David Weikersdorfer , Qian Lin , Aman Jhunjhunwala , Emilie Lucie Eloïse Wirbel , Sangmin Oh , Minwoo Park , Gyeong Woo Cheon , Arthur Henry Rajala , Bor-Jeng Chen
IPC: G01S15/931 , G01S15/86
CPC classification number: G01S15/931 , G01S15/86 , G01S2015/938
Abstract: In various examples, techniques for sensor-fusion based object detection and/or free-space detection using ultrasonic sensors are described. Systems may receive sensor data generated using one or more types of sensors of a machine. In some examples, the systems may then process at least a portion of the sensor data to generate input data, where the input data represents one or more locations of one or more objects within an environment. The systems may then input at least a portion of the sensor data and/or at least a portion of the input data into one or more neural networks that are trained to output one or more maps or other output representations associated with the environment. In some examples, the map(s) may include a height, an occupancy, and/or height/occupancy map generated, e.g., from a birds-eye-view perspective. The machine may use these outputs to perform one or more operations
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公开(公告)号：US20220284624A1
公开(公告)日：2022-09-08
申请号：US17191543
申请日：2021-03-03
Applicant: NVIDIA Corporation
Inventor： Sravya Nimmagadda , David Weikersdorfer
Abstract: An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.
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Patent Agency Ranking
公开(公告)号：US20240037788A1
公开(公告)日：2024-02-01
申请号：US18487800
申请日：2023-10-16
Applicant: Nvidia Corporation
Inventor： Sravya Nimmagadda , David Weikersdorfer
CPC classification number: G06T7/75 , B25J9/1697 , G06N3/08 , G06V20/10 , G06N3/045
Abstract: An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.
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公开(公告)号：US20240176017A1
公开(公告)日：2024-05-30
申请号：US18060376
申请日：2022-11-30
Applicant: NVIDIA Corporation
Inventor： David Weikersdorfer , Qian Lin , Aman Jhunjhunwala , Emilie Lucie Eloïse Wirbel , Sangmin Oh , Minwoo Park , Gyeong Woo Cheon , Arthur Henry Rajala , Bor-Jeng Chen
IPC: G01S15/931 , G01S15/86
CPC classification number: G01S15/931 , G01S15/86 , G01S2015/938
Abstract: In various examples, techniques for sensor-fusion based object detection and/or free-space detection using ultrasonic sensors are described. Systems may receive sensor data generated using one or more types of sensors of a machine. In some examples, the systems may then process at least a portion of the sensor data to generate input data, where the input data represents one or more locations of one or more objects within an environment. The systems may then input at least a portion of the sensor data and/or at least a portion of the input data into one or more neural networks that are trained to output one or more maps or other output representations associated with the environment. In some examples, the map(s) may include a height, an occupancy, and/or height/occupancy map generated, e.g., from a birds-eye-view perspective. The machine may use these outputs to perform one or more operations.
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公开(公告)号：US20250139827A1
公开(公告)日：2025-05-01
申请号：US19005645
申请日：2024-12-30
Applicant: NVIDIA Corporation
Inventor： Sravya Nimmagadda , David Weikersdorfer
Abstract: An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.
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公开(公告)号：US11823415B2
公开(公告)日：2023-11-21
申请号：US17191543
申请日：2021-03-03
Applicant: NVIDIA Corporation
Inventor： Sravya Nimmagadda , David Weikersdorfer
CPC classification number: G06T7/75 , B25J9/1697 , G06N3/045 , G06N3/08 , G06V20/10
Abstract: An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.
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