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:
Aug. 20, 2024

Filed:

Jun. 25, 2021
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:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/62 (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); G06K 9/00 (2022.01); G06K 9/46 (2006.01); 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); G06V 20/58 (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

Systems, methods, and other embodiments described herein relate to evaluating a perception network in relation to the accuracy of depth estimates and object detections. In one embodiment, a method includes segmenting range data associated with an image according to bounding boxes of objects identified in the image to produce masked data. The method includes comparing the masked data with corresponding depth estimates in the depth map according to an evaluation mask that correlates the depth estimates with the depth map. The method includes providing a metric that quantifies the comparing to assess a network that generated the depth map and the bounding boxes.


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