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:
Jul. 02, 2019

Filed:

Jun. 18, 2015
Applicant:

Peking University Shenzhen Graduate School, Shenzhen, CN;

Inventors:

Wenmin Wang, Shenzhen, CN;

Mingmin Zhen, Shenzhen, CN;

Ronggang Wang, Shenzhen, CN;

Ge Li, Shenzhen, CN;

Shengfu Dong, Shenzhen, CN;

Zhenyu Wang, Shenzhen, CN;

Ying Li, Shenzhen, CN;

Wen Gao, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/46 (2006.01); G06T 7/64 (2017.01); G06K 9/42 (2006.01); G06T 5/40 (2006.01); G06K 9/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/4604 (2013.01); G06K 9/00201 (2013.01); G06K 9/42 (2013.01); G06K 9/4671 (2013.01); G06T 5/40 (2013.01); G06T 7/64 (2017.01); G06T 2207/10028 (2013.01);
Abstract

A method and a device for extracting local features of a 3D point cloud are disclosed. Angle information and the concavo-convex information about a feature point to be extracted and a point of an adjacent body element are calculated based on a local reference system corresponding to the points of each body element. The feature relation between the two points can be calculated accurately. The property of invariance in translation and rotation is possessed. Since concavo-convex information about a local point cloud is contained during extraction, the inaccurate extraction caused by ignoring concavo-convex ambiguity in previous 3D local feature description is resolved. During normalization processing, exponential normalization processing and second-normal-form normalization are adopted, which solves the problem of inaccurate similarity calculation caused by a circumstance that a few elements in a vector are too large or too small during feature extraction, thus improving accuracy of extracted three-dimensional local features.


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