The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 20, 2024

Filed:

Aug. 28, 2020
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Tilman Wekel, Sunnyvale, CA (US);

Sangmin Oh, San Jose, CA (US);

David Nister, Bellevue, WA (US);

Joachim Pehserl, Lynnwood, WA (US);

Neda Cvijetic, East Palo Alto, CA (US);

Ibrahim Eden, Redmond, WA (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01S 7/00 (2006.01); G01S 7/48 (2006.01); G01S 17/894 (2020.01); G01S 7/481 (2006.01); G01S 17/931 (2020.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G01S 7/28 (2006.01);
U.S. Cl.
CPC ...
G01S 7/4802 (2013.01); G01S 7/481 (2013.01); G01S 17/894 (2020.01); G01S 17/931 (2020.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G01S 7/28 (2013.01);
Abstract

In various examples, a deep neural network (DNN) may be used to detect and classify animate objects and/or parts of an environment. The DNN may be trained using camera-to-LiDAR cross injection to generate reliable ground truth data for LiDAR range images. For example, annotations generated in the image domain may be propagated to the LiDAR domain to increase the accuracy of the ground truth data in the LiDAR domain—e.g., without requiring manual annotation in the LiDAR domain. Once trained, the DNN may output instance segmentation masks, class segmentation masks, and/or bounding shape proposals corresponding to two-dimensional (2D) LiDAR range images, and the outputs may be fused together to project the outputs into three-dimensional (3D) LiDAR point clouds. This 2D and/or 3D information output by the DNN may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.


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