Surface reconstruction based on radial basis functions network

Han Bo Liu; Xin Wang; Xiao Jun Wu; Wen Yi Qiang

doi:10.1007/11760191_181

Surface reconstruction based on radial basis functions network

Han Bo Liu^*
, Xin Wang
, Xiao Jun Wu
, Wen Yi Qiang

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Harbin Institute of Technology

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

2 Scopus citations

Abstract

A new method for arbitrary 3d-object reconstruction in unknown environment is proposed in this paper. The implicit surface is reconstructed based on radial basis functions network from range scattered data. For the property of locality of radial basis function, the method is fast and robust with respect to large data. Furthermore, an adapted K-Means algorithm is used to reduce RBF centers for reconstruction. Experiment results show that the presented approach is helpful in speed improvement and is a good solution for large data reconstruction.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
Publisher	Springer Verlag
Pages	1242-1247
Number of pages	6
ISBN (Print)	3540344829, 9783540344827
DOIs	https://doi.org/10.1007/11760191_181
State	Published - 2006
Externally published	Yes
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: 28 May 2006 → 1 Jun 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3973 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/Territory	China
City	Chengdu
Period	28/05/06 → 1/06/06

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10.1007/11760191_181

Cite this

Liu, H. B., Wang, X., Wu, X. J., & Qiang, W. Y. (2006). Surface reconstruction based on radial basis functions network. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III (pp. 1242-1247). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS). Springer Verlag. https://doi.org/10.1007/11760191_181

Liu, Han Bo ; Wang, Xin ; Wu, Xiao Jun et al. / Surface reconstruction based on radial basis functions network. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. pp. 1242-1247 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e889804dfa2f4d1db811a0204673848b,

title = "Surface reconstruction based on radial basis functions network",

abstract = "A new method for arbitrary 3d-object reconstruction in unknown environment is proposed in this paper. The implicit surface is reconstructed based on radial basis functions network from range scattered data. For the property of locality of radial basis function, the method is fast and robust with respect to large data. Furthermore, an adapted K-Means algorithm is used to reduce RBF centers for reconstruction. Experiment results show that the presented approach is helpful in speed improvement and is a good solution for large data reconstruction.",

author = "Liu, \{Han Bo\} and Xin Wang and Wu, \{Xiao Jun\} and Qiang, \{Wen Yi\}",

year = "2006",

doi = "10.1007/11760191\_181",

language = "英语",

isbn = "3540344829",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1242--1247",

booktitle = "Advances in Neural Networks - ISNN 2006",

address = "德国",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

}

Liu, HB, Wang, X, Wu, XJ & Qiang, WY 2006, Surface reconstruction based on radial basis functions network. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3973 LNCS, Springer Verlag, pp. 1242-1247, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 28/05/06. https://doi.org/10.1007/11760191_181

Surface reconstruction based on radial basis functions network. / Liu, Han Bo; Wang, Xin; Wu, Xiao Jun et al.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. p. 1242-1247 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS).

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

TY - GEN

T1 - Surface reconstruction based on radial basis functions network

AU - Liu, Han Bo

AU - Wang, Xin

AU - Wu, Xiao Jun

AU - Qiang, Wen Yi

PY - 2006

Y1 - 2006

N2 - A new method for arbitrary 3d-object reconstruction in unknown environment is proposed in this paper. The implicit surface is reconstructed based on radial basis functions network from range scattered data. For the property of locality of radial basis function, the method is fast and robust with respect to large data. Furthermore, an adapted K-Means algorithm is used to reduce RBF centers for reconstruction. Experiment results show that the presented approach is helpful in speed improvement and is a good solution for large data reconstruction.

AB - A new method for arbitrary 3d-object reconstruction in unknown environment is proposed in this paper. The implicit surface is reconstructed based on radial basis functions network from range scattered data. For the property of locality of radial basis function, the method is fast and robust with respect to large data. Furthermore, an adapted K-Means algorithm is used to reduce RBF centers for reconstruction. Experiment results show that the presented approach is helpful in speed improvement and is a good solution for large data reconstruction.

UR - https://www.scopus.com/pages/publications/33745928128

U2 - 10.1007/11760191_181

DO - 10.1007/11760191_181

M3 - 会议稿件

AN - SCOPUS:33745928128

SN - 3540344829

SN - 9783540344827

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1242

EP - 1247

BT - Advances in Neural Networks - ISNN 2006

PB - Springer Verlag

T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

Y2 - 28 May 2006 through 1 June 2006

ER -

Liu HB, Wang X, Wu XJ, Qiang WY. Surface reconstruction based on radial basis functions network. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag. 2006. p. 1242-1247. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11760191_181

Surface reconstruction based on radial basis functions network

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