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:
Jan. 21, 2025

Filed:

Mar. 17, 2022
Applicant:

Guangzhou Xiaopeng Autopilot Technology Co., Ltd., Guangdong, CN;

Inventors:

Runyu Mao, Guangdong, CN;

Chen Bai, Guangdong, CN;

Yatong An, Guangdong, CN;

Cheng Lu, Guangdong, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06F 17/18 (2006.01); G06V 10/75 (2022.01); G06V 10/776 (2022.01); G06V 10/778 (2022.01);
U.S. Cl.
CPC ...
G06V 10/751 (2022.01); G06F 17/18 (2013.01); G06V 10/776 (2022.01); G06V 10/778 (2022.01);
Abstract

A system for three-dimensional geometric guided student-teacher feature matching includes a multi-modal teacher model configured to determine feature matching between a pair of RGB-D images, each RGB-D image being a combination of a RGB image and its corresponding depth image; a mono-modal student model configured to determine feature matching from the pair of RGB images and the teacher model, the teacher model guiding the student model to learn RGB-induced depth information for the feature matching on both coarse and fine levels; a coarse-level knowledge transfer loss function for determining loss of transferring coarse-level matching knowledge from the teacher model to the student model; and a fine-level knowledge transfer loss function for determining loss of transferring fine-level matching knowledge from the teacher model to the student model, wherein the fine-level knowledge transfer loss function guides the student model to learn a fine-level prediction distribution with priority.


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