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:
Jan. 16, 2024

Filed:

Jul. 26, 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:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/55 (2017.01); B60R 1/00 (2022.01); G06T 3/00 (2006.01); G05D 1/02 (2020.01); G06N 3/08 (2023.01); G06T 7/579 (2017.01); 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 self-supervised depth and ego-motion estimation is described. The method includes determining a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The method also includes generating a self-occlusion mask by manually segmenting self-occluded areas of images captured by the multi-camera rig of the ego vehicle. The method further includes multiplying the multi-camera photometric loss with the self-occlusion mask to form a self-occlusion masked photometric loss. The method also includes training a depth estimation model and an ego-motion estimation model according to the self-occlusion masked photometric loss. The method further includes predicting a 360° point cloud of a scene surrounding the ego vehicle according to the depth estimation model and the ego-motion estimation model.


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