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公开(公告)号：US11651215B2
公开(公告)日：2023-05-16
申请号：US17109421
申请日：2020-12-02
Applicant: NVIDIA Corporation
Inventor： Minwoo Park , Yilin Yang , Xiaolin Lin , Abhishek Bajpayee , Hae-Jong Seo , Eric Jonathan Yuan , Xudong Chen
IPC: G06N3/08 , G06V20/58 , G06V20/56 , G06F18/23 , G06F18/214 , G06V10/762 , G06V10/764 , G06V10/82 , G06V10/44 , G06V10/26 , G06V10/46 , G05D1/00 , G06N3/045 , G06V10/75 , G06V10/774 , G06V10/94
CPC classification number: G06N3/08 , G05D1/0088 , G06F18/214 , G06F18/23 , G06N3/045 , G06V10/26 , G06V10/454 , G06V10/46 , G06V10/757 , G06V10/763 , G06V10/764 , G06V10/774 , G06V10/82 , G06V10/955 , G06V20/582 , G06V20/588 , G05D2201/0213 , G06V10/471
Abstract: In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.
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公开(公告)号：US20210166052A1
公开(公告)日：2021-06-03
申请号：US17109421
申请日：2020-12-02
Applicant: NVIDIA Corporation
Inventor： Minwoo Park , Yilin Yang , Xiaolin Lin , Abhishek Bajpayee , Hae-Jong Seo , Eric Jonathan Yuan , Xudong Chen
Abstract: In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.
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公开(公告)号：US20230214654A1
公开(公告)日：2023-07-06
申请号：US18174856
申请日：2023-02-27
Applicant: c/o NVIDIA Corporation
Inventor： Minwoo Park , Yilin Yang , Xiaolin Lin , Abhishek Bajpayee , Hae-Jong Seo , Eric Jonathan Yuan , Xudong Chen
CPC classification number: G06V20/58 , G06V20/588 , B60W60/001 , B60W2420/42
Abstract: In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.
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