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公开(公告)号：US20210097346A1
公开(公告)日：2021-04-01
申请号：US17119971
申请日：2020-12-11
Applicant: NVIDIA Corporation
Inventor： Jonathan Tremblay , Aayush Prakash , Mark A. Brophy , Varun Jampani , Cem Anil , Stanley Thomas Birchfield , Thang Hong To , David Jesus Acuna Marrero
Abstract: Training deep neural networks requires a large amount of labeled training data. Conventionally, labeled training data is generated by gathering real images that are manually labelled which is very time-consuming. Instead of manually labelling a training dataset, domain randomization technique is used generate training data that is automatically labeled. The generated training data may be used to train neural networks for object detection and segmentation (labelling) tasks. In an embodiment, the generated training data includes synthetic input images generated by rendering three-dimensional (3D) objects of interest in a 3D scene. In an embodiment, the generated training data includes synthetic input images generated by rendering 3D objects of interest on a 2D background image. The 3D objects of interest are objects that a neural network is trained to detect and/or label.
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公开(公告)号：US20190251397A1
公开(公告)日：2019-08-15
申请号：US16256820
申请日：2019-01-24
Applicant: NVIDIA Corporation
Inventor： Jonathan Tremblay , Aayush Prakash , Mark A. Brophy , Varun Jampani , Cem Anil , Stanley Thomas Birchfield , Thang Hong To , David Jesus Acuna Marrero
CPC classification number: G06K9/6257 , G06K9/6228 , G06K2209/21 , G06N3/04 , G06N3/084 , G06T15/04 , G06T15/20 , G06T15/50 , G06T2210/12
Abstract: Training deep neural networks requires a large amount of labeled training data. Conventionally, labeled training data is generated by gathering real images that are manually labelled which is very time-consuming. Instead of manually labelling a training dataset, domain randomization technique is used generate training data that is automatically labeled. The generated training data may be used to train neural networks for object detection and segmentation (labelling) tasks. In an embodiment, the generated training data includes synthetic input images generated by rendering three-dimensional (3D) objects of interest in a 3D scene. In an embodiment, the generated training data includes synthetic input images generated by rendering 3D objects of interest on a 2D background image. The 3D objects of interest are objects that a neural network is trained to detect and/or label.
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公开(公告)号：US11715251B2
公开(公告)日：2023-08-01
申请号：US17507620
申请日：2021-10-21
Applicant: NVIDIA Corporation
Inventor： Jonathan Tremblay , Aayush Prakash , Mark A. Brophy , Varun Jampani , Cem Anil , Stanley Thomas Birchfield , Thang Hong To , David Jesus Acuna Marrero
IPC: G06T15/00 , G06T15/04 , G06T15/50 , G06T15/20 , G06F18/214 , G06F18/211 , G06V10/774 , G06V10/82 , G06N3/04 , G06N3/084
CPC classification number: G06T15/00 , G06F18/211 , G06F18/2148 , G06T15/04 , G06T15/20 , G06T15/50 , G06V10/7747 , G06V10/82 , G06N3/04 , G06N3/084 , G06T2210/12 , G06V2201/07
Abstract: Training deep neural networks requires a large amount of labeled training data. Conventionally, labeled training data is generated by gathering real images that are manually labelled which is very time-consuming. Instead of manually labelling a training dataset, domain randomization technique is used generate training data that is automatically labeled. The generated training data may be used to train neural networks for object detection and segmentation (labelling) tasks. In an embodiment, the generated training data includes synthetic input images generated by rendering three-dimensional (3D) objects of interest in a 3D scene. In an embodiment, the generated training data includes synthetic input images generated by rendering 3D objects of interest on a 2D background image. The 3D objects of interest are objects that a neural network is trained to detect and/or label.
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公开(公告)号：US11182649B2
公开(公告)日：2021-11-23
申请号：US17119971
申请日：2020-12-11
Applicant: NVIDIA Corporation
Inventor： Jonathan Tremblay , Aayush Prakash , Mark A. Brophy , Varun Jampani , Cem Anil , Stanley Thomas Birchfield , Thang Hong To , David Jesus Acuna Marrero
Abstract: Training deep neural networks requires a large amount of labeled training data. Conventionally, labeled training data is generated by gathering real images that are manually labelled which is very time-consuming. Instead of manually labelling a training dataset, domain randomization technique is used generate training data that is automatically labeled. The generated training data may be used to train neural networks for object detection and segmentation (labelling) tasks. In an embodiment, the generated training data includes synthetic input images generated by rendering three-dimensional (3D) objects of interest in a 3D scene. In an embodiment, the generated training data includes synthetic input images generated by rendering 3D objects of interest on a 2D background image. The 3D objects of interest are objects that a neural network is trained to detect and/or label.
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