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:
Dec. 24, 2024

Filed:

Jun. 11, 2020
Applicant:

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

Inventors:

Vitor Guizilini, Santa Clara, CA (US);

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

Sudeep Pillai, Santa Clara, 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 20/64 (2022.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06N 3/045 (2023.01); G06N 3/084 (2023.01); G06N 3/088 (2023.01); G06T 7/269 (2017.01); G06T 7/50 (2017.01); G06T 7/70 (2017.01); G06V 10/764 (2022.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
G06V 20/64 (2022.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 7/269 (2017.01); G06T 7/70 (2017.01); G06V 10/764 (2022.01); G06V 20/56 (2022.01); G06V 20/647 (2022.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A two dimensional image can be received. A depth map can be produced, via a first neural network, from the two dimensional image. A bird's eye view image can be produced, via a second neural network, from the depth map. The second neural network can implement a machine learning algorithm that preserves spatial gradient information associated with one or more objects included in the depth map and causes a position of a pixel in an object, included in the bird's eye view image, to be represented by a differentiable function. Three dimensional objects can be detected, via a third neural network, from the two dimensional image, the bird's eye view image, and the spatial gradient information. A combination of the first neural network, the second neural network, and the third neural network can be end-to-end trainable and can be included in a perception system.


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