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:
Jul. 30, 2024

Filed:

Oct. 06, 2021
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Murugan Sankaradas, Dayton, NJ (US);

Kunal Rao, Monroe, NJ (US);

Yi Yang, Princeton, NJ (US);

Biplob Debnath, Princeton, NJ (US);

Utsav Drolia, Milipitas, CA (US);

Srimat Chakradhar, Manalapan, NJ (US);

Amit Redkar, Princeton, NJ (US);

Ravi Kailasam Rajendran, Princeton, NJ (US);

Assignee:

NEC Corporation, Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/25 (2023.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06T 7/50 (2017.01); G06T 11/00 (2006.01); G06V 10/40 (2022.01); G06V 20/00 (2022.01);
U.S. Cl.
CPC ...
G06F 18/253 (2023.01); G06F 18/2148 (2023.01); G06N 3/045 (2023.01); G06T 7/50 (2017.01); G06T 11/00 (2013.01); G06V 10/40 (2022.01); G06V 20/00 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2210/12 (2013.01);
Abstract

A method for real-time cross-spectral object association and depth estimation is presented. The method includes synthesizing, by a cross-spectral generative adversarial network (CS-GAN), visual images from different data streams obtained from a plurality of different types of sensors, applying a feature-preserving loss function resulting in real-time pairing of corresponding cross-spectral objects, and applying dual bottleneck residual layers with skip connections to accelerate real-time inference and to accelerate convergence during model training.


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