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. 27, 2024

Filed:

Apr. 18, 2023
Applicant:

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

Inventors:

Gunnar Hovden, Los Gatos, CA (US);

Mykhaylo Kurinnyy, Milpitas, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/778 (2022.01); G06F 18/21 (2023.01); G06T 15/04 (2011.01); G06T 15/20 (2011.01); G06T 17/00 (2006.01); G06T 19/00 (2011.01); G06V 20/00 (2022.01); G06V 20/10 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06V 10/7796 (2022.01); G06F 18/2193 (2023.01); G06T 15/04 (2013.01); G06T 15/205 (2013.01); G06T 17/00 (2013.01); G06T 19/003 (2013.01); G06V 20/10 (2022.01); G06V 20/35 (2022.01); G06T 2210/04 (2013.01); G06V 20/647 (2022.01);
Abstract

Techniques are provided for increasing the accuracy of automated classifications produced by a machine learning engine. Specifically, the classification produced by a machine learning engine for one photo-realistic image is adjusted based on the classifications produced by the machine learning engine for other photo-realistic images that correspond to the same portion of a 3D model that has been generated based on the photo-realistic images. Techniques are also provided for using the classifications of the photo-realistic images that were used to create a 3D model to automatically classify portions of the 3D model. The classifications assigned to the various portions of the 3D model in this manner may also be used as a factor for automatically segmenting the 3D model.


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