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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2022 - 3

Attention-Based Joint Semantic-Instance Segmentation of 3D Point Clouds

HAO, W. See more information about HAO, W. on SCOPUS See more information about HAO, W. on IEEExplore See more information about HAO, W. on Web of Science, WANG, H. See more information about  WANG, H. on SCOPUS See more information about  WANG, H. on SCOPUS See more information about WANG, H. on Web of Science, LIANG, W. See more information about  LIANG, W. on SCOPUS See more information about  LIANG, W. on SCOPUS See more information about LIANG, W. on Web of Science, ZHAO, M. See more information about  ZHAO, M. on SCOPUS See more information about  ZHAO, M. on SCOPUS See more information about ZHAO, M. on Web of Science, XIAO, Z. See more information about XIAO, Z. on SCOPUS See more information about XIAO, Z. on SCOPUS See more information about XIAO, Z. on Web of Science
 
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Download PDF pdficon (3,331 KB) | Citation | Downloads: 782 | Views: 1,833

Author keywords
computer graphics, object segmentation, feature extraction, pattern recognition, machine learning

References keywords
point(24), segmentation(22), pattern(19), instance(17), vision(15), semantic(15), recognition(15), cvpr(14), clouds(11), learning(8)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 19 - 28
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02003
Web of Science Accession Number: 000810486800003
SCOPUS ID: 85131726998

Abstract
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In this paper, we propose an attention-based instance and semantic segmentation joint approach, termed ABJNet, for addressing the instance and semantic segmentation of 3D point clouds simultaneously. First, a point feature enrichment (PFE) module is used to enrich the segmentation networks input data by indicating the relative importance of each points neighbors. Then, a more efficient attention pooling operation is designed to establish a novel module for extracting point cloud features. Finally, an efficient attention-based joint segmentation module (ABJS) is proposed for combining semantic features and instance features in order to improve both segmentation tasks. We evaluate the proposed attention-based joint semantic-instance segmentation neural network (ABJNet) on a variety of indoor scene datasets, including S3DIS and ScanNet V2. Experimental results demonstrate that our method outperforms the start-of-the-art method in 3D instance segmentation and significantly outperforms it in 3D semantic segmentation.


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Cited-By CrossRef

[1] A Model for Urban Environment Instance Segmentation with Data Fusion, Du, Kaiyue, Meng, Jin, Meng, Xin, Wang, Shifeng, Yang, Jinhua, Sensors, ISSN 1424-8220, Issue 13, Volume 23, 2023.
Digital Object Identifier: 10.3390/s23136141
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