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Normal estimation via shifted neighborhood for point cloud

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成果类型:
期刊论文、会议论文
作者:
Cao, Junjie;Chen, He;Zhang, Jie;Li, Yujiao;Liu, Xiuping;Zou, Changqing
通讯作者:
Zhang, J
作者机构:
[Li, Yujiao; Chen, He; Cao, Junjie; Liu, Xiuping] School of Mathematical Sciences, Dalian University of Technology, Dalian, China
[Zhang, Jie] School of Mathematics, Liaoning Normal University, Dalian, China
[Zou, Changqing] Hengyang Normal University, China
[Cao, Junjie] College of Mathematics and Information Science, Nanchang Hangkong University, Nanchang, China
通讯机构:
[Zhang, J] Liaoning Normal Univ, Sch Math, Dalian, Peoples R China.
语种:
英文
关键词:
Normal estimation;Point cloud;Neighborhood shift
期刊:
Journal of Computational and Applied Mathematics
ISSN:
0377-0427
年:
2018
卷:
329
页码:
57-67
文献类别:
WOS:Article;Proceedings Paper;EI:Journal article (JA)
所属学科:
ESI学科类别:数学;WOS学科类别:Mathematics, Applied
入藏号:
WOS:000413613900006;EI:20174204278383
基金类别:
NSFC [61363048, 61572099, 61370143]; Fundamental Research Funds for the Central Universities [DUT16QY02]; Foundation of Liaoning Educational Committee [L201683663]; Hengyang Normal University, China [15B22]
机构署名:
本校为其他机构
摘要:
For accurately estimating the normal of a point, the structure of its neighborhood has to be analyzed. All the previous methods use some neighborhood centering at the point, which is prone to be sampled from different surface patches when the point is near sharp features. Then more inaccurate normals or higher computation cost may be unavoidable. To conquer this problem, we present a fast and quality normal estimator based on neighborhood shift. Instead of using the neighborhood centered at the point, we wish to locate a neighborhood containing the point but clear of sharp features, which is usually not centering at the point. Two specific neighborhood shift techniques are designed in view of the complex structure of sharp features and the characteristic of raw point clouds. The experiments show that our method out-performs previous normal estimators in either quality or running time, even in the presence of noise and anisotropic sampling. (C) 2017 Elsevier B.V. All rights reserved.
参考文献:
Boulch A, 2012, COMPUT GRAPH FORUM, V31, P1765, DOI 10.1111/j.1467-8659.2012.03181.x
Cazals F, 2005, COMPUT AIDED GEOM D, V22, P121, DOI 10.1016/j.cagd.2004.09.004
Cazals F., 2003, Symposium on Geometry Processing, P177
Cho H, 2014, ACM T GRAPHIC, V33, DOI 10.1145/2601097.2601188
Fleishman S, 2005, ACM T GRAPHIC, V24, P544, DOI 10.1145/1073204.1073227

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