Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

Yuichiro Toda; Zhaojie Ju; Hui Yu; Naoyuki  Takesue; Kazuyoshi Wada; Naoyuki Kubota

doi:10.1109/HSI.2016.7529667

Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

Yuichiro Toda
, Zhaojie Ju
, Hui Yu
, Naoyuki Takesue
, Kazuyoshi Wada
, Naoyuki Kubota

Tokyo Metropolitan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This paper proposes a real-time feature extraction and segmentation method for a 3D point cloud. First of all, we apply Growing Neural Gas with Utility (GNG-U) to the point cloud for learning a topological structure. However, the standard GNG-U cannot learn the topological structure of 3D space environment and color information simultaneously. To this end, we then modify the GNG-U algorithm by using a weight vector. we propose a surface feature extraction and segmentation method by efficiently utilizing the topological structure. Our segmentation method is based on a region growing method whose similarity value uses the inner value of two normal vectors connected by the topological structure. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.

Original language	English
Title of host publication	2016 9th International Conference on Human System Interactions (HSI)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	418-422
ISBN (Electronic)	978-1-5090-1729-4
ISBN (Print)	978-1-5090-1730-0
DOIs	https://doi.org/10.1109/HSI.2016.7529667
Publication status	Published - 4 Aug 2016
Event	9th International Conference on Human System Interactions: HSI 2016 - University of Portsmouth, Portsmouth, United Kingdom Duration: 6 Jul 2016 → 8 Jul 2016

Conference

Conference	9th International Conference on Human System Interactions
Abbreviated title	HSI 2016
Country/Territory	United Kingdom
City	Portsmouth
Period	6/07/16 → 8/07/16

Keywords

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formatting
style
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insert (key words)

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10.1109/HSI.2016.7529667

Real-time 3D point cloud segmentation using Growing Neural Gas with Utility
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Accepted author manuscript (Post-print), 2.25 MB

Cite this

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title = "Real-time 3D point cloud segmentation using Growing Neural Gas with Utility",

abstract = "This paper proposes a real-time feature extraction and segmentation method for a 3D point cloud. First of all, we apply Growing Neural Gas with Utility (GNG-U) to the point cloud for learning a topological structure. However, the standard GNG-U cannot learn the topological structure of 3D space environment and color information simultaneously. To this end, we then modify the GNG-U algorithm by using a weight vector. we propose a surface feature extraction and segmentation method by efficiently utilizing the topological structure. Our segmentation method is based on a region growing method whose similarity value uses the inner value of two normal vectors connected by the topological structure. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.",

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note = "9th International Conference on Human System Interactions : HSI 2016, HSI 2016 ; Conference date: 06-07-2016 Through 08-07-2016",

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Toda, Y, Ju, Z, Yu, H, Takesue, N, Wada , K & Kubota, N 2016, Real-time 3D point cloud segmentation using Growing Neural Gas with Utility. in 2016 9th International Conference on Human System Interactions (HSI). Institute of Electrical and Electronics Engineers Inc., pp. 418-422, 9th International Conference on Human System Interactions, Portsmouth, United Kingdom, 6/07/16. https://doi.org/10.1109/HSI.2016.7529667

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AU - Ju, Zhaojie

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AU - Takesue, Naoyuki

AU - Wada , Kazuyoshi

AU - Kubota, Naoyuki

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N2 - This paper proposes a real-time feature extraction and segmentation method for a 3D point cloud. First of all, we apply Growing Neural Gas with Utility (GNG-U) to the point cloud for learning a topological structure. However, the standard GNG-U cannot learn the topological structure of 3D space environment and color information simultaneously. To this end, we then modify the GNG-U algorithm by using a weight vector. we propose a surface feature extraction and segmentation method by efficiently utilizing the topological structure. Our segmentation method is based on a region growing method whose similarity value uses the inner value of two normal vectors connected by the topological structure. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.

AB - This paper proposes a real-time feature extraction and segmentation method for a 3D point cloud. First of all, we apply Growing Neural Gas with Utility (GNG-U) to the point cloud for learning a topological structure. However, the standard GNG-U cannot learn the topological structure of 3D space environment and color information simultaneously. To this end, we then modify the GNG-U algorithm by using a weight vector. we propose a surface feature extraction and segmentation method by efficiently utilizing the topological structure. Our segmentation method is based on a region growing method whose similarity value uses the inner value of two normal vectors connected by the topological structure. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.

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BT - 2016 9th International Conference on Human System Interactions (HSI)

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Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

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Conference

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