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:
May. 20, 2025

Filed:

May. 19, 2022
Applicant:

Beijing University of Civil Engineering and Architecture, Beijing, CN;

Inventors:

Jianghong Zhao, Beijing, CN;

Xintong Dou, Beijing, CN;

Yingxue Zhao, Beijing, CN;

Yinrui Wang, Beijing, CN;

Ruiju Zhang, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/764 (2022.01); G06T 3/40 (2006.01); G06V 10/44 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/764 (2022.01); G06T 3/40 (2013.01); G06V 10/44 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06T 2200/04 (2013.01);
Abstract

A full-automatic classification method for a three-dimensional point cloud, including: acquiring a three-dimensional point cloud dataset; performing down-sampling on a three-dimensional point cloud represented by the three-dimensional point cloud dataset, selecting some points in the three-dimensional point cloud as sampling points, constructing a point cloud area group based on each sampling point, extracting a global feature of each point cloud area group, and replacing the point cloud area group where the sampling point is located with the sampling point; performing up-sampling on the three-dimensional point cloud, and performing splicing fusion on the global features of the point cloud area group where each point in the three-dimensional point cloud is located; performing category discrimination on each point in the three-dimensional point cloud; performing statistics on the number of points contained in each category, and selecting the category with the largest number of points as the category of the three-dimensional point cloud.


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