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. 27, 2023

Filed:

Jul. 15, 2021
Applicant:

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

Inventors:

Vitor Guizilini, Santa Clara, CA (US);

Rares Andrei Ambrus, Santa Clara, CA (US);

Adrien David Gaidon, San Francisco, CA (US);

Igor Vasiljevic, Chicago, IL (US);

Gregory Shakhnarovich, Chicago, IL (US);

Assignee:

TOYOTA RESEARCH INSTITUTE, INC., Los Altos, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/55 (2017.01); G06N 3/08 (2023.01); G06T 7/579 (2017.01); B60R 1/00 (2022.01); G06T 3/00 (2006.01); G05D 1/02 (2020.01); G06T 7/292 (2017.01); G06T 7/11 (2017.01); B60W 60/00 (2020.01); G06T 3/40 (2006.01); G06F 18/214 (2023.01); H04N 23/90 (2023.01);
U.S. Cl.
CPC ...
G06T 7/55 (2017.01); B60R 1/00 (2013.01); B60W 60/001 (2020.02); G05D 1/0212 (2013.01); G05D 1/0246 (2013.01); G06F 18/214 (2023.01); G06F 18/2148 (2023.01); G06N 3/08 (2013.01); G06T 3/0012 (2013.01); G06T 3/0093 (2013.01); G06T 3/40 (2013.01); G06T 7/11 (2017.01); G06T 7/292 (2017.01); G06T 7/579 (2017.01); H04N 23/90 (2023.01); B60R 2300/102 (2013.01); B60W 2420/42 (2013.01); G05D 2201/0213 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30244 (2013.01); G06T 2207/30252 (2013.01);
Abstract

A method for multi-camera self-supervised depth evaluation is described. The method includes training a self-supervised depth estimation model and an ego-motion estimation model according to a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The method also includes generating a single-scale correction factor according to a depth map of each camera of the multi-camera rig during a time-step. The method further includes predicting a 360° point cloud of a scene surrounding the ego vehicle according to the self-supervised depth estimation model and the ego-motion estimation model. The method also includes scaling the 360° point cloud according to the single-scale correction factor to form an aligned 360° point cloud.


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