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. 25, 2024

Filed:

May. 28, 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); 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

Systems, methods, and other embodiments described herein relate to performing depth estimation and object detection using a common network architecture. In one embodiment, a method includes generating, using a backbone of a combined network, a feature map at multiple scales from an input image. The method includes decoding, using a top-down pathway of the combined network, the feature map to provide features at the multiple scales. The method includes generating, using a head of the combined network, a depth map from the features for a scene depicted in the input image, and bounding boxes identifying objects in the input image.


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