[1]顾晓清,马正华,侯振杰,等.一种快速散乱点自适应滤波方法[J].常州大学学报(自然科学版),2012,(01):69-72.
　GU Xiao-Qing,MA Zheng-Hua,HOU Zhen-Jie,et al.Fast Self-Adaptive Method for Scatter Point Cloud Denoising[J].Journal of Changzhou University(Natural Science Edition),2012,(01):69-72.
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一种快速散乱点自适应滤波方法()
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常州大学学报(自然科学版)[ISSN:2095-0411/CN:32-1822/N]

卷:
期数:
2012年01期
页码:
69-72
栏目:
计算机与信息工程
出版日期:
2012-01-01

文章信息/Info

Title:
Fast Self-Adaptive Method for Scatter Point Cloud Denoising
作者:
顾晓清; 马正华;  侯振杰; 倪彤光
常州大学 信息科学与工程学院,江苏 常州 213164
Author(s):
GU Xiao-Qing;  MA Zheng-Hua;  HOU Zhen-Jie; NI Tong-Guang
School of Information Science & Engineering, Changzhou University, Changzhou 213164, China
关键词:
点模型;  自适应;  高斯核函数;  柯西核函数;  降噪
Keywords:
point-sampled model;  self-adaptive;  gauss kernel;  cauchy kernel;  denosing
分类号:
TP 391.41
文献标志码:
A
摘要:
消除噪声是构造完美三维模型过程中必不可少的一步。梁新合提出基于自适应最优邻域的散乱点云降噪算法。但该算法效率较低,为此提出用准柯西函数取代该算法所采用的高斯函数,提高了算法效率。实验结果表明,本文算法能在有效剔除点模型表面噪声的同时较好地保持表面的尖锐特征。
Abstract:
Denoising is an essential step in creating perfect point-sampled models. Liang Xinhe extends image mean-shift filtering to 3D surface smoothing by taking the vertex normal and curvature as range component and the vertex position as the spatial component, which is not efficient. For this reason, this paper proposes to use quasi-Cauchy kernel to replace the Gauss kernel used in the Guofei.Hu'algorithm. Experiments show that our method can smooth the noise efficiently and preserve the sharp features of the surface effectively.

参考文献/References:

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[6]梁新合,梁晋,郭成,等. 基于自适应最优邻域的散乱点云降噪技术研究[J]. 中国机械工程,2010,21(6),639-641.
[7]陈韶椿,金小刚,冯结青,等. 快速网格去噪算法[J]. 中国图象图形学报, 2004, 9(8):1 320-1 325.

备注/Memo

备注/Memo:
基金项目:国家自然科学基金项目(61063021) 作者简介:顾晓清(1981—),女,江苏常州人,讲师。
更新日期/Last Update: 2012-01-01