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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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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: 314 | Views: 233

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.


References | Cited By  «-- Click to see who has cited this paper

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[2] Y. Nie, J. Hou, X. Han and M. Nießner, "RfD-Net: Point scene understanding by semantic instance reconstruction," IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 4606-4616.
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[CrossRef]


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[7] Y. Su, W. Liu, Z. Yuan, et al., "DLA-Net: Learning dual local attention features for semantic segmentation of large-scale building facade point clouds," Pattern Recognit. 123: 108372, 2022.
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[8] J. Hou, A. Dai, M. Niesner, "3D-SIS: 3D semantic instance segmentation of RGB-D scans," Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019, pp. 4421-4430,
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[CrossRef] [SCOPUS Times Cited 19]


[12] W. Wang, R. Yu, Q. Huang, U. Neumann, "SGPN: Similarity group proposal network for 3D point cloud instance segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition. 2020, pp.2569-2578.
[CrossRef] [Web of Science Times Cited 213] [SCOPUS Times Cited 255]


[13] C. R. Qi, H. Su, K. Mo, L. J. Guibas, "Pointnet: Deep learning on point sets for 3D classification and segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, pp.77-85.
[CrossRef] [Web of Science Times Cited 1035] [SCOPUS Times Cited 4666]


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[15] B. Graham, M. Engelcke, L. Van Der Maaten, "3D semantic segmentation with submanifold sparse convolutional networks," Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, pp.9224-9232.
[CrossRef] [Web of Science Times Cited 358] [SCOPUS Times Cited 455]


[16] Z. Liang, M. Yang, H. Li, C. Wang, "3D instance embedding learning with a structure-aware loss function for point cloud segmentation," IEEE Robotics and Automation Letters. vol.5, no.3, pp.4915-4922, 2020.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]


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[18] L. Jiang, H. Zhao, S. Shi, S. Liu, C. W. Fu, J. Jia, "Pointgroup: Dual-set point grouping for 3D instance segmentation," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020, pp. 4866-4875.
[CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 72]


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[CrossRef]


[20] Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, M. Bennamoun, "Deep learning for 3D point clouds: A survey," IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 12, pp. 4338-4364, 2020.
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[21] Q. H. Pham, T. Nguyen, B. S. Hua, G. Roig, S. K. Yeung, "JSIS3D: Joint semantic-instance segmentation of 3D point clouds with multi-task pointwise networks and multi-value conditional random fields," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, pp.8827-8836.
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[CrossRef]


[23] G. Wu, Z. Pan, P. Jiang, C. Tu, "Bi-Directional attention for joint instance and semantic segmentation in point clouds," Proceedings of the Asian Conference on Computer Vision, 2020, pp. 1-17.
[CrossRef]


[24] F. Chen, F. Wu, G. Gao, Y. Ji, J. Xu, G. Jiang, X. Jing, "JSPNet: Learning joint semantic & instance segmentation of point clouds via feature self-similarity and cross-task probability," Pattern Recognit. vol. 122, no. 108250, 2022.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[25] C. Chen, L. Z. Fragonara, A. Tsourdos, "GAPNet: Graph attention based point neural network for exploiting local feature of point cloud," arXiv preprint arXiv:1905.08705, 2019.
[CrossRef]


[26] Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, A. Markham, "RandLA-Net: efficient semantic segmentation of large-scale point clouds," Proceedings of the Computer Vision and Pattern Recognition, 2020, pp. 11105-11114.
[CrossRef] [SCOPUS Times Cited 361]


[27] I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, S. Savarese, "3D semantic parsing of large-scale indoor spaces," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016, pp. 1534-1543.
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[28] A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, M. Nießner, "ScanNet: Richly-annotated 3D reconstructions of indoor scenes," Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, pp. 2432-2443.
[CrossRef] [Web of Science Times Cited 722] [SCOPUS Times Cited 890]


[29] L. Du, J. Tan, X. Xue, L. Chen, "3DCFS: Fast and robust joint 3D semantic-instance segmentation via coupled feature selection," IEEE International Conference on Robotics and Automation. 2020, pp. 6868-6875.
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[30] M. Zhong, G. Zeng, "Joint Semantic-Instance Segmentation of 3D point clouds: Instance separation and semantic fusion," 25th International Conference on Pattern Recognition. 2021, pp. 6616-6623.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]




References Weight

Web of Science® Citations for all references: 10,476 TCR
SCOPUS® Citations for all references: 17,498 TCR

Web of Science® Average Citations per reference: 338 ACR
SCOPUS® Average Citations per reference: 564 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-09-25 14:01 in 201 seconds.




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