3D point cloud classification model based on local location information

Teng Chen; Lifen Jiang; Fengbo Zheng; Panpan Wu; Bowei Chen; Kang Li

doi:10.1117/12.2656820

20 October 2022 3D point cloud classification model based on local location information

Teng Chen, Lifen Jiang, Fengbo Zheng, Panpan Wu, Bowei Chen, Kang Li

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245129 (2022) https://doi.org/10.1117/12.2656820
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

For the existing 3D point cloud model classification method PointNet only extracts global feature information, but for the problem of insufficient local feature information extraction, a network that can take into account the combination of global features and local features is proposed, so as to realize 3D point Classification of cloud models. This paper proposes a classification network based on local location information. First, the point cloud model is sampled in multiple groups to obtain the center point of each group of samples, and then the radius is set to obtain its spherical neighborhood, and the points in the neighborhood and their center points are used as local location features. In information fusion, the acquired local information features are used as high-dimensional features of the neighborhood space through feature mapping. By analogy, multiple sets of sampled local features are integrated to realize the classification of the point cloud model. The experimental results show that the classification network can achieve a classification accuracy of 92.5% on the ModelNet40 data set, which is higher than the current mainstream algorithm.

Citation Download Citation

Teng Chen, Lifen Jiang, Fengbo Zheng, Panpan Wu, Bowei Chen, and Kang Li "3D point cloud classification model based on local location information", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245129 (20 October 2022); https://doi.org/10.1117/12.2656820

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

3D modeling

Clouds

Data modeling

Feature extraction

Convolutional neural networks

Statistical modeling

Associative arrays

Show All Keywords

Keywords/Phrases

Search In:

Publication Years