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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.

Patent No.:

US 12008818 B1

PDFFull Text
Date of Patent:
Jun. 11, 2024

Filed:

Jul. 23, 2021

Systems and methods to train a prediction system for depth perception

Applicant:

Toyota Research Institute, Inc., Los Altos, CA (US);

Inventors:

Rares A. Ambrus, San Francisco, CA (US);

Dennis Park, Fremont, CA (US);

Vitor Guizilini, Santa Clara, CA (US);

Jie Li, Los Altos, CA (US);

Adrien David Gaidon, Mountain View, CA (US);

Assignee:

Toyota Research Institute, Inc., Los Altos, CA (US);

Attorneys:

Christopher G. Darrow

Darrow Mustafa PC

Primary Examiner:

Michelle M Entezari Hausmann

Int. Cl.
CPC ...
G06V 20/58 (2022.01); G01S 17/42 (2006.01); G01S 17/89 (2020.01); G01S 17/931 (2020.01); G06F 18/21 (2023.01); G06F 18/2113 (2023.01); G06F 18/214 (2023.01); G06F 18/25 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/50 (2017.01); G06V 10/46 (2022.01); G06V 10/75 (2022.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
G06V 20/58 (2022.01); G01S 17/42 (2013.01); G01S 17/89 (2013.01); G01S 17/931 (2020.01); G06F 18/2113 (2023.01); G06F 18/2155 (2023.01); G06F 18/217 (2023.01); G06F 18/251 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/50 (2017.01); G06V 10/462 (2022.01); G06V 10/757 (2022.01); G06V 20/56 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30248 (2013.01);
Abstract

System, methods, and other embodiments described herein relate to a manner of training a depth prediction system using bounding boxes. In one embodiment, a method includes segmenting an image to mask areas beyond bounding boxes and identify unmasked areas within the bounding boxes. The method also includes training a depth model using depth losses from comparing weighted points associated with pixels of the image within the unmasked areas to ground-truth depth. The method also includes providing the depth model for object detection.

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