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:
Aug. 06, 2024

Filed:

Mar. 15, 2022
Applicant:

Matterport, Inc., Sunnyvale, CA (US);

Inventor:

David Alan Gausebeck, Sunnyvale, CA (US);

Assignee:

Matterport, Inc., Sunnyvale, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 19/20 (2011.01); G06T 7/521 (2017.01); G06T 7/579 (2017.01); G06T 7/593 (2017.01); G06T 17/00 (2006.01); G06T 19/00 (2011.01); H04N 13/10 (2018.01); H04N 13/106 (2018.01); H04N 13/156 (2018.01); H04N 13/204 (2018.01); H04N 13/246 (2018.01); H04N 13/271 (2018.01); H04N 13/00 (2018.01);
U.S. Cl.
CPC ...
G06T 19/20 (2013.01); G06T 7/521 (2017.01); G06T 7/579 (2017.01); G06T 7/593 (2017.01); G06T 17/00 (2013.01); G06T 19/006 (2013.01); H04N 13/10 (2018.05); H04N 13/106 (2018.05); H04N 13/156 (2018.05); H04N 13/204 (2018.05); H04N 13/246 (2018.05); H04N 13/271 (2018.05); G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/10052 (2013.01); G06T 2210/04 (2013.01); H04N 2013/0081 (2013.01); H04N 2213/001 (2013.01);
Abstract

The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system comprising a processor, a panoramic image, and employing, by the system, a three-dimensional data from two-dimensional data (3D-from-2D) convolutional neural network model to derive three-dimensional data from the panoramic image, wherein the 3D-from-2D convolutional neural network model employs convolutional layers that wrap around the panoramic image as projected on a two-dimensional plane to facilitate deriving the three-dimensional data.


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