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:
Oct. 24, 2023

Filed:

May. 25, 2021
Applicant:

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

Inventors:

Dennis Park, Fremont, CA (US);

Rares A. Ambrus, San Francisco, 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:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/58 (2022.01); G06T 7/50 (2017.01); G01S 17/931 (2020.01); G06T 7/11 (2017.01); G06V 10/46 (2022.01); G06V 20/56 (2022.01); G01S 17/42 (2006.01); G01S 17/89 (2020.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 7/10 (2017.01); G06N 20/00 (2019.01); G06V 10/75 (2022.01); G06F 18/21 (2023.01); G06F 18/25 (2023.01); G06F 18/2113 (2023.01); G06F 18/214 (2023.01);
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 18/217 (2023.01); G06F 18/2113 (2023.01); G06F 18/2155 (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

Described are systems and methods for self-learned label refinement of a training set. In on example, a system includes a processor and a memory having a training set generation module that causes the processor to train a model using an image as an input to the model and 2D bounding based on 3D bounding boxes as ground truths, select a first subset from predicted 2D bounding boxes previously outputted by the model, retrain the model using the image as the input and the first subset as ground truths, select a second set of predicted 2D bounding boxes previously outputted by the model, and generate the training set by selecting the 3D bounding boxes from a master set of 3D bounding boxes that have corresponding 2D bounding boxes that form the second subset.


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