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. 13, 2024

Filed:

Oct. 07, 2021
Applicant:

Sri International, Menlo Park, CA (US);

Inventors:

Han-Pang Chiu, West Windsor, NJ (US);

Junjiao Tian, Atlanta, GA (US);

Zachary Seymour, Pennington, NJ (US);

Niluthpol C. Mithun, Lawrenceville, NJ (US);

Alex Krasner, Princeton, NJ (US);

Mikhail Sizintsev, Princeton, NJ (US);

Abhinav Rajvanshi, Plainsboro, NJ (US);

Kevin Kaighn, Medord, NJ (US);

Philip Miller, Yardley, NJ (US);

Ryan Villamil, Plainsboro, NJ (US);

Supun Samarasekera, Skillman, NJ (US);

Assignee:

SRI International, Menlo Park, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/174 (2017.01); G06T 3/40 (2006.01); G06T 7/38 (2017.01); G06T 7/50 (2017.01);
U.S. Cl.
CPC ...
G06T 7/174 (2017.01); G06T 3/40 (2013.01); G06T 7/38 (2017.01); G06T 7/50 (2017.01); G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20112 (2013.01);
Abstract

A method, machine readable medium and system for RGBD semantic segmentation of video data includes determining semantic segmentation data and depth segmentation data for less than all classes for images of each frame of a first video, determining semantic segmentation data and depth segmentation data for images of each key frame of a second video including a synchronous combination of respective frames of the RGB video and the depth-aware video in parallel to the determination of the semantic segmentation data and the depth segmentation data for each frame of the first video, temporally and geometrically aligning respective frames of the first video and the second video, and predicting semantic segmentation data and depth segmentation data for images of a subsequent frame of the first video based on the determination of the semantic segmentation data and depth segmentation data for images of a key frame of the second video.


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