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. 24, 2024

Filed:

Aug. 05, 2022
Applicant:

GM Global Technology Operations Llc, Detroit, MI (US);

Inventors:

Siddhartha Gupta, Rochester Hills, MI (US);

Wei Tong, Troy, MI (US);

Upali P. Mudalige, Rochester Hills, MI (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/778 (2022.01); G06T 7/11 (2017.01); G06V 10/25 (2022.01); G06V 10/26 (2022.01); G06V 10/74 (2022.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
G06V 10/778 (2022.01); G06T 7/11 (2017.01); G06V 10/25 (2022.01); G06V 10/26 (2022.01); G06V 10/761 (2022.01); G06V 10/774 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01);
Abstract

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to determine a pairwise region of interest feature similarity based on features extracted from a first cropped image portion and corresponding point cloud data and features extracted from a second cropped image portion and corresponding point cloud data. The processor is also programmed to determine a loss using a loss function based on the pairwise region of interest feature similarity, wherein the loss function corresponds to at least one a first deep neural network or a second deep neural network. The processor is also programmed to update at least one weight of the at least one of the first deep neural network or the second deep neural network based on the loss.


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