首页 | 本学科首页   官方微博 | 高级检索  
     

一种带噪声的密集三角网格细分曲面拟合算法
引用本文:吴剑煌,刘伟军,王天然,赵吉宾. 一种带噪声的密集三角网格细分曲面拟合算法[J]. 软件学报, 2007, 18(2): 442-452
作者姓名:吴剑煌  刘伟军  王天然  赵吉宾
作者单位:中国科学院,沈阳自动化所,先进制造技术实验室,辽宁,沈阳,110016;中国科学院,研究生院,北京,100049;中国科学院,沈阳自动化所,先进制造技术实验室,辽宁,沈阳,110016
摘    要:实现了一个从带噪声的密集三角形拟合出带尖锐特征的细分曲面拟合系统.该系统包括了一种改进的基于图像双边滤波器的网格噪声去除方法,模型的尖锐特征提取以及保持尖锐特征的网格简化和拓扑优化.为了处理局部细节特征和模型数据量问题,提出了自适应细分方法,并将根据给定精度估计最少细分深度引入到细分曲面拟合系统中,使得拟合得到的细分曲面模型具有良好的细节特征和数据量小等特点.大量3D模型实验结果和实际工程应用结果表明了该细分曲面拟合系统的有效性.

关 键 词:细分曲面  网格噪声去除  自适应细分  深度估计  曲面拟合
收稿时间:2006-02-22
修稿时间:2006-04-05

A Fitting Algorithm of Subdivision Surface from Noising and Dense Triangular Meshes
WU Jian-Huang,LIU Wei-Jun,WANG Tian-Ran and ZHAO Ji-Bin. A Fitting Algorithm of Subdivision Surface from Noising and Dense Triangular Meshes[J]. Journal of Software, 2007, 18(2): 442-452
Authors:WU Jian-Huang  LIU Wei-Jun  WANG Tian-Ran  ZHAO Ji-Bin
Affiliation:Laboratory of Advanced Manufacture Technology, Shenyang Institute of Automation, The Chinese Academy of Sciences, Shenyang 110016, China
Abstract:A fitting system is developed to fit subdivision surface with sharp feature from noising and dense triangular meshes of arbitrary topology. The system includes an improved mesh denoising method based on bilateral filtering of images, sharp features extraction, feature-preserving mesh simplification and topological optimization. An estimating method for Loop subdivision surface is introduced to predict how many subdivision iterations are necessary to meet a user-defined tolerance. The method of adaptive subdivision is proposed during the fitting process to handle local detailed surface features. Both experimental results and practical applications in engineering demonstrate that the system can effectively achieve a good quality of the fitting subdivision surface with nice details while using few facets in the approximation.
Keywords:subdivision surface  mesh denoising  adaptive subdivision  depth estimating  surface fitting
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《软件学报》浏览原始摘要信息
点击此处可从《软件学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号