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:
Oct. 14, 2025

Filed:

Jan. 29, 2021
Applicant:

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

Inventors:

William Castillo, Belmont, CA (US);

Brandon Scott, New York, NY (US);

Alrik Firl, San Francisco, CA (US);

David Royston Cutts, San Francisco, CA (US);

Jonathan Mark Igner, San Francisco, CA (US);

Dario Rethage, Kendall Park, NJ (US);

Domenico Curro, San Francisco, CA (US);

Giridhar Murali, Sunnyvale, CA (US);

Panfeng Li, San Francisco, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04N 23/60 (2023.01); G06F 3/16 (2006.01); G06T 7/11 (2017.01); G06T 7/12 (2017.01); G06T 7/174 (2017.01); G06T 7/277 (2017.01); G06T 7/73 (2017.01); G06T 15/00 (2011.01); G06T 17/00 (2006.01); G06V 10/26 (2022.01); G06V 10/44 (2022.01); G06V 30/19 (2022.01); G06V 30/414 (2022.01); H04N 23/63 (2023.01);
U.S. Cl.
CPC ...
H04N 23/64 (2023.01); G06T 7/11 (2017.01); G06T 7/12 (2017.01); G06T 7/174 (2017.01); G06T 7/277 (2017.01); G06T 7/74 (2017.01); G06T 17/00 (2013.01); G06V 10/26 (2022.01); G06V 10/44 (2022.01); H04N 23/635 (2023.01); G06F 3/167 (2013.01); G06T 15/00 (2013.01); G06T 2207/20072 (2013.01); G06T 2207/20084 (2013.01); G06T 2210/00 (2013.01); G06V 30/19013 (2022.01); G06V 30/19107 (2022.01); G06V 30/414 (2022.01);
Abstract

Disclosed are techniques for enhancing two-dimensional (2D) image capture of subjects (e.g., a physical structure, such as a residential building) to maximize the feature correspondences available for three-dimensional (3D) model reconstruction. More specifically, disclosed is a computer-vision network configured to provide viewfinder interfaces and analyses to guide the improved capture of an intended subject for specified purposes. Additionally, the computer-vision network can be configured to generate a metric representing a quality of feature correspondences between images of a complete set of images used for reconstructing a 3D model of a physical structure. The computer-vision network can also be configured to generate feedback at or before image capture time to guide improvements to the quality of feature correspondences between a pair of images.


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