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:
Jun. 11, 2024

Filed:

Jul. 20, 2021
Applicant:

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

Inventors:

Shubham Shrivastava, Sunnyvale, CA (US);

Gaurav Pandey, College Station, TX (US);

Punarjay Chakravarty, Campbell, CA (US);

Assignee:

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

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/73 (2017.01); B60W 10/04 (2006.01); B60W 10/18 (2012.01); B60W 10/20 (2006.01); G05B 13/02 (2006.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G06T 7/74 (2017.01); B60W 10/04 (2013.01); B60W 10/18 (2013.01); B60W 10/20 (2013.01); G05B 13/027 (2013.01); G06N 3/084 (2013.01); G06T 7/75 (2017.01); G06T 2207/10024 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30204 (2013.01); G06T 2207/30232 (2013.01);
Abstract

A depth image of an object can be input to a deep neural network to determine a first four degree-of-freedom pose of the object. The first four degree-of-freedom pose and a three-dimensional model of the object can be input to a silhouette rendering program to determine a first two-dimensional silhouette of the object. A second two-dimensional silhouette of the object can be determined based on thresholding the depth image. A loss function can be determined based on comparing the first two-dimensional silhouette of the object to the second two-dimensional silhouette of the object. Deep neural network parameters can be optimized based on the loss function and the deep neural network can be output.


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