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:
Apr. 26, 2022

Filed:

Mar. 26, 2021
Applicant:

Niantic, Inc., San Francisco, CA (US);

Inventors:

James Watson, London, GB;

Michael David Firman, London, GB;

Gabriel J. Brostow, London, GB;

Daniyar Turmukhambetov, London, GB;

Assignee:

Niantic, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 13/268 (2018.01); G06T 7/593 (2017.01); G06T 7/80 (2017.01); G06T 7/73 (2017.01); H04N 13/00 (2018.01);
U.S. Cl.
CPC ...
H04N 13/268 (2018.05); G06T 7/593 (2017.01); G06T 7/73 (2017.01); G06T 7/85 (2017.01); G06T 2207/10021 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); H04N 2013/0081 (2013.01);
Abstract

A method for training a depth estimation model with depth hints is disclosed. For each image pair: for a first image, a depth prediction is determined by the depth estimation model and a depth hint is obtained; the second image is projected onto the first image once to generate a synthetic frame based on the depth prediction and again to generate a hinted synthetic frame based on the depth hint; a primary loss is calculated with the synthetic frame; a hinted loss is calculated with the hinted synthetic frame; and an overall loss is calculated for the image pair based on a per-pixel determination of whether the primary loss or the hinted loss is smaller, wherein if the hinted loss is smaller than the primary loss, then the overall loss includes the primary loss and a supervised depth loss between depth prediction and depth hint. The depth estimation model is trained by minimizing the overall losses for the image pairs.


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