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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.

Patent No.:

US 11842495 B1

PDFFull Text
Date of Patent:
Dec. 12, 2023

Filed:

Mar. 08, 2022

Systems and methods for transparent object segmentation using polarization cues

Applicant:

Intrinsic Innovation Llc, Mountain View, CA (US);

Inventors:

Agastya Kalra, Nepean, CA;

Vage Taamazyan, Moscow, RU;

Supreeth Krishna Rao, San Jose, CA (US);

Kartik Venkataraman, San Jose, CA (US);

Ramesh Raskar, Cambridge, MA (US);

Achuta Kadambi, Los Altos Hills, CA (US);

Assignee:

Intrinsic Innovation LLC, Mountain View, CA (US);

Attorney:

Fish & Richardson P.C.

Primary Examiner:

Xuemei G Chen

Int. Cl.
CPC ...
G06T 7/11 (2017.01); B25J 9/16 (2006.01); G05B 13/02 (2006.01); G06F 18/214 (2023.01); G06F 18/25 (2023.01); G06N 3/045 (2023.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06V 10/60 (2022.01); G06V 10/147 (2022.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); B25J 9/1697 (2013.01); G05B 13/027 (2013.01); G06F 18/214 (2023.01); G06F 18/253 (2023.01); G06N 3/045 (2023.01); G06V 10/147 (2022.01); G06V 10/454 (2022.01); G06V 10/60 (2022.01); G06V 10/82 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A computer-implemented method for computing a prediction on images of a scene includes: receiving one or more polarization raw frames of a scene, the polarization raw frames being captured with a polarizing filter at a different linear polarization angle; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and computing a prediction regarding one or more optically challenging objects in the scene based on the one or more first tensors in the one or more polarization representation spaces.

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