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:
Jan. 07, 2025

Filed:

Oct. 28, 2021
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Kang Wang, Bellevue, WA (US);

Yue Wu, Mountain View, CA (US);

Minwoo Park, Saratoga, CA (US);

Gang Pan, Fremont, CA (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/20 (2006.01); B60W 30/14 (2006.01); B60W 40/06 (2012.01); B60W 50/06 (2006.01); B60W 60/00 (2020.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06N 3/08 (2023.01); G06T 7/11 (2017.01); G06T 7/40 (2017.01);
U.S. Cl.
CPC ...
G06T 17/20 (2013.01); B60W 30/143 (2013.01); B60W 40/06 (2013.01); B60W 50/06 (2013.01); B60W 60/0015 (2020.02); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06T 7/40 (2013.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); G06T 2200/08 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a simulated environment. For example, a simulation may be run to simulate a virtual world or environment, render frames of virtual sensor data (e.g., images), and generate corresponding depth maps and segmentation masks (identifying a component of the simulated environment such as a road). To generate input training data, 3D structure estimation may be performed on a rendered frame to generate a representation of a 3D surface structure of the road. To generate corresponding ground truth training data, a corresponding depth map and segmentation mask may be used to generate a dense representation of the 3D surface structure.


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