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:
Mar. 16, 2021

Filed:

May. 08, 2019
Applicant:

Ford Global Technologies, Llc, Dearborn, MI (US);

Inventors:

Nikita Jaipuria, Union City, CA (US);

Gautham Sholingar, Sunnyvale, CA (US);

Vidya Nariyambut Murali, Sunnyvale, CA (US);

Assignee:

FORD GLOBAL TECHNOLOGIES, LLC, Dearborn, MI (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/593 (2017.01); G06T 11/00 (2006.01); G06K 9/62 (2006.01); G05D 1/02 (2020.01);
U.S. Cl.
CPC ...
G06K 9/00805 (2013.01); G05D 1/0221 (2013.01); G05D 1/0251 (2013.01); G06K 9/6255 (2013.01); G06T 7/593 (2017.01); G06T 11/001 (2013.01); G05D 2201/0213 (2013.01); G06T 2207/10012 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30261 (2013.01);
Abstract

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate a pair of synthetic stereo images and a corresponding synthetic depth map with an image synthesis engine wherein the synthetic stereo images correspond to real stereo images acquired by a stereo camera and the synthetic depth map is a three-dimensional (3D) map corresponding to a 3D scene viewed by the stereo camera and process each image of the pair of synthetic stereo images pair independently using a generative adversarial network (GAN) to generate a fake image, wherein the fake image corresponds to one of the synthetic stereo images. The instructions can further include instructions to process a stereo pair of fake images to form a fake depth map, compare the fake depth map to the synthetic depth map using a Siamese one-shot neural network, train a deep neural network to determine an object using one or more of the fake images generated by the GAN, and operate a vehicle based on the object.


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