Revolutionizing 3D point cloud segmentation: an innovative network utilizing multi-scale feature fusion

Hainan Wang; Yiming Tang; Xiang Huang; Mei Wu; Yue Meng

doi:10.1117/12.3009223

16 October 2023 Revolutionizing 3D point cloud segmentation: an innovative network utilizing multi-scale feature fusion

Hainan Wang, Yiming Tang, Xiang Huang, Mei Wu, Yue Meng

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032J (2023) https://doi.org/10.1117/12.3009223
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

The emergence of 3D point cloud analysis has brought about new opportunities and challenges in various fields such as autonomous driving, digital twins, and virtual reality. Accurate segmentation is crucial to 3D point cloud analysis, but challenges arise due to the lack of topological information, complex shapes, and sparsity and unevenness in point sampling. To address these problems, a novel point cloud segmentation network called PCSNet (Point Cloud Segmentation Network) has been proposed. PCSNet combines global and local features to determine the overall shape and detailed local information, respectively, through an encoder-decoder architecture that incorporates multi-scale feature fusion. The encoder progressively extracts local center points, fuses local features, and models global features with the transformer to construct multi-scale topological and semantic information. The decoder then recovers the original point cloud and incorporates multi-scale features by upsampling for accurate segmentation. PCSNet outperforms state-of-the-art point cloud segmentation approaches on two widely used benchmark datasets (ShapeNetPart and S3DIS).

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hainan Wang, Yiming Tang, Xiang Huang, Mei Wu, and Yue Meng "Revolutionizing 3D point cloud segmentation: an innovative network utilizing multi-scale feature fusion", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032J (16 October 2023); https://doi.org/10.1117/12.3009223

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
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Point clouds

Transformers

Feature fusion

Feature extraction

Semantics

Spherical lenses

Visualization

Show All Keywords

Keywords/Phrases

Search In:

Publication Years