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. 10, 2023

Filed:

Sep. 29, 2020
Applicant:

Tsinghua University, Beijing, CN;

Inventors:

Lu Fang, Beijing, CN;

Mengqi Ji, Beijing, CN;

Shi Mao, Beijing, CN;

Qionghai Dai, Beijing, CN;

Assignee:

TSINGHUA UNIVERSITY, Beijing, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G02B 27/48 (2006.01); G01N 21/39 (2006.01); G06T 5/00 (2006.01); G06F 18/214 (2023.01); G06F 18/25 (2023.01); G06V 10/56 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 10/60 (2022.01); G06V 10/143 (2022.01);
U.S. Cl.
CPC ...
G02B 27/48 (2013.01); G01N 21/39 (2013.01); G06F 18/214 (2023.01); G06F 18/251 (2023.01); G06T 5/009 (2013.01); G06T 7/0004 (2013.01); G06V 10/143 (2022.01); G06V 10/56 (2022.01); G06V 10/60 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/803 (2022.01); G06V 10/82 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

The present disclosure provides a material identification method and a device based on laser speckle and modal fusion, an electronic device and a non-transitory computer readable storage medium. The method includes: performing data acquisition on an object by using a structured light camera to obtain a color modal image, a depth modal image and an infrared modal image; preprocessing the color modal image, the depth modal image and the infrared modal image; and inputting the color modal image, the depth modal image and the infrared modal image preprocessed into a preset depth neural network for training, to learn a material characteristic from a speckle structure and a coupling relation between color modal and depth modal, to generate a material classification model for classifying materials, and to generate a material prediction result in testing by the material classification model of the object.


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