Hierarchical Grow Network for Point Cloud Segmentation

Jiajing Chen; Burak Kakillioglu; Senem Velipasalar

doi:10.1109/IEEECONF51394.2020.9443497

Hierarchical Grow Network for Point Cloud Segmentation

Jiajing Chen, Burak Kakillioglu, Senem Velipasalar

Research output: Chapter in Book/Entry/Poem › Conference contribution

2 Scopus citations

Abstract

In this paper, a novel deep learning-based point cloud segmentation model, referred to as the hierarchical grow network, is proposed. This model is based on the PointNet segmentation model.Different from PointNet, the entire point cloud is divided into several clusters, which are then combined at different hierarchical levels. This way, information from different receptive fields is used to make the final segmentation. The proposed hierarchical growing approach can also be used with other 3D feature extraction networks. Experiments performed on the indoor S3DIS database show that our proposed approach provides improvement and higher accuracy compared to the PointNet model.

Original language	English (US)
Title of host publication	Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1558-1562
Number of pages	5
ISBN (Electronic)	9780738131269
DOIs	https://doi.org/10.1109/IEEECONF51394.2020.9443497
State	Published - Nov 1 2020
Externally published	Yes
Event	54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States Duration: Nov 1 2020 → Nov 5 2020

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2020-November
ISSN (Print)	1058-6393

Conference

Conference	54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/Territory	United States
City	Pacific Grove
Period	11/1/20 → 11/5/20

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF51394.2020.9443497

Cite this

Chen, J., Kakillioglu, B., & Velipasalar, S. (2020). Hierarchical Grow Network for Point Cloud Segmentation. In M. B. Matthews (Ed.), Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 (pp. 1558-1562). Article 9443497 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2020-November). IEEE Computer Society. https://doi.org/10.1109/IEEECONF51394.2020.9443497

Hierarchical Grow Network for Point Cloud Segmentation. / Chen, Jiajing; Kakillioglu, Burak; Velipasalar, Senem.
Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020. ed. / Michael B. Matthews. IEEE Computer Society, 2020. p. 1558-1562 9443497 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2020-November).

Research output: Chapter in Book/Entry/Poem › Conference contribution

Chen, J, Kakillioglu, B & Velipasalar, S 2020, Hierarchical Grow Network for Point Cloud Segmentation. in MB Matthews (ed.), Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020., 9443497, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2020-November, IEEE Computer Society, pp. 1558-1562, 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020, Pacific Grove, United States, 11/1/20. https://doi.org/10.1109/IEEECONF51394.2020.9443497

Chen J, Kakillioglu B, Velipasalar S. Hierarchical Grow Network for Point Cloud Segmentation. In Matthews MB, editor, Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020. IEEE Computer Society. 2020. p. 1558-1562. 9443497. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/IEEECONF51394.2020.9443497

@inproceedings{b2d0e560241f4a6d9dab5ec3ae40f6a9,

title = "Hierarchical Grow Network for Point Cloud Segmentation",

abstract = "In this paper, a novel deep learning-based point cloud segmentation model, referred to as the hierarchical grow network, is proposed. This model is based on the PointNet segmentation model.Different from PointNet, the entire point cloud is divided into several clusters, which are then combined at different hierarchical levels. This way, information from different receptive fields is used to make the final segmentation. The proposed hierarchical growing approach can also be used with other 3D feature extraction networks. Experiments performed on the indoor S3DIS database show that our proposed approach provides improvement and higher accuracy compared to the PointNet model.",

author = "Jiajing Chen and Burak Kakillioglu and Senem Velipasalar",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 ; Conference date: 01-11-2020 Through 05-11-2020",

year = "2020",

month = nov,

day = "1",

doi = "10.1109/IEEECONF51394.2020.9443497",

language = "English (US)",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "1558--1562",

editor = "Matthews, {Michael B.}",

booktitle = "Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020",

address = "United States",

}

TY - GEN

T1 - Hierarchical Grow Network for Point Cloud Segmentation

AU - Chen, Jiajing

AU - Kakillioglu, Burak

AU - Velipasalar, Senem

PY - 2020/11/1

Y1 - 2020/11/1

N2 - In this paper, a novel deep learning-based point cloud segmentation model, referred to as the hierarchical grow network, is proposed. This model is based on the PointNet segmentation model.Different from PointNet, the entire point cloud is divided into several clusters, which are then combined at different hierarchical levels. This way, information from different receptive fields is used to make the final segmentation. The proposed hierarchical growing approach can also be used with other 3D feature extraction networks. Experiments performed on the indoor S3DIS database show that our proposed approach provides improvement and higher accuracy compared to the PointNet model.

AB - In this paper, a novel deep learning-based point cloud segmentation model, referred to as the hierarchical grow network, is proposed. This model is based on the PointNet segmentation model.Different from PointNet, the entire point cloud is divided into several clusters, which are then combined at different hierarchical levels. This way, information from different receptive fields is used to make the final segmentation. The proposed hierarchical growing approach can also be used with other 3D feature extraction networks. Experiments performed on the indoor S3DIS database show that our proposed approach provides improvement and higher accuracy compared to the PointNet model.

UR - http://www.scopus.com/inward/record.url?scp=85107761797&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85107761797&partnerID=8YFLogxK

U2 - 10.1109/IEEECONF51394.2020.9443497

DO - 10.1109/IEEECONF51394.2020.9443497

M3 - Conference contribution

AN - SCOPUS:85107761797

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1558

EP - 1562

BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020

A2 - Matthews, Michael B.

PB - IEEE Computer Society

T2 - 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020

Y2 - 1 November 2020 through 5 November 2020

ER -

Hierarchical Grow Network for Point Cloud Segmentation

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this