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:
Dec. 05, 2023

Filed:

Dec. 17, 2020
Applicant:

Board of Trustees of the University of Illinois, Urbana, IL (US);

Inventors:

Junfeng Guan, Champaign, IL (US);

Seyedsohrab Madani, Champaign, IL (US);

Suraj S. Jog, Champaign, IL (US);

Haitham Al Hassanieh, Champaign, IL (US);

Saurabh Gupta, Champaign, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/00 (2006.01); G06T 7/593 (2017.01); G06T 15/06 (2011.01); G06T 15/04 (2011.01); G06V 20/64 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
G06T 17/00 (2013.01); G06T 7/593 (2017.01); G06T 15/04 (2013.01); G06T 15/06 (2013.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/64 (2022.01); G06V 20/647 (2022.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A method includes receiving data including a plurality of data items, each data item of the plurality of data items including a three-dimensional (3D) radar heat map of an object and a corresponding two-dimensional (2D) image of the object captured by a stereo camera, inputting the training dataset into a machine learning model including a neural network (NN) that generates, from the 3D radar heat map, a 2D depth map for the object and outputs a probability that the 2D depth map is the corresponding 2D image of the object, and training the machine learning model based on a training dataset to generate a trained machine learning model that iteratively learns to generate an updated 2D depth map that approximates the corresponding 2D image. The training dataset includes the plurality of data items, the 2D depth map and the probability.


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