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公开(公告)号:US20190340728A1
公开(公告)日:2019-11-07
申请号:US16130871
申请日:2018-09-13
Applicant: NVIDIA Corp.
Inventor: Varun Jampani , Deqing Sun , Ming-Yu Liu , Jan Kautz
Abstract: A superpixel sampling network utilizes a neural network coupled to a differentiable simple linear iterative clustering component to determine pixel-superpixel associations from a set of pixel features output by the neural network. The superpixel sampling network computes updated superpixel centers and final pixel-superpixel associations over a number of iterations.
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公开(公告)号:US11636668B2
公开(公告)日:2023-04-25
申请号:US15986267
申请日:2018-05-22
Applicant: NVIDIA Corp.
Inventor: Varun Jampani , Hang Su , Deqing Sun , Ming-Hsuan Yang , Jan Kautz
IPC: G06V10/82 , G06N3/04 , G06T7/521 , G06V20/64 , G06V30/19 , G06N3/08 , G06T7/11 , G06T7/174 , G06F18/2415 , G06F18/2413
Abstract: A method includes filtering a point cloud transformation of a 3D object to generate a 3D lattice and processing the 3D lattice through a series of bilateral convolution networks (BCL), each BCL in the series having a lower lattice feature scale than a preceding BCL in the series. The output of each BCL in the series is concatenated to generate an intermediate 3D lattice. Further filtering of the intermediate 3D lattice generates a first prediction of features of the 3D object.
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公开(公告)号:US10789678B2
公开(公告)日:2020-09-29
申请号:US16130871
申请日:2018-09-13
Applicant: NVIDIA Corp.
Inventor: Varun Jampani , Deqing Sun , Ming-Yu Liu , Jan Kautz
Abstract: A superpixel sampling network utilizes a neural network coupled to a differentiable simple linear iterative clustering component to determine pixel-superpixel associations from a set of pixel features output by the neural network. The superpixel sampling network computes updated superpixel centers and final pixel-superpixel associations over a number of iterations.
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公开(公告)号:US20190147302A1
公开(公告)日:2019-05-16
申请号:US15986267
申请日:2018-05-22
Applicant: NVIDIA Corp.
Inventor: Varun Jampani , Hang Su , Deqing Sun , Ming-Hsuan Yang , Jan Kautz
Abstract: A method includes filtering a point cloud transformation of a 3D object to generate a 3D lattice and processing the 3D lattice through a series of bilateral convolution networks (BCL), each BCL in the series having a lower lattice feature scale than a preceding BCL in the series. The output of each BCL in the series is concatentated to generate an intermediate 3D lattice. Further filtering of the intermediate 3D lattice generates a first prediction of features of the 3D object.
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