CASIA OpenIR  > 毕业生  > 博士学位论文
三维几何建模中的曲面重建与简化研究
其他题名Surface Reconstruction and Simplification in 3D Geometric Modeling
高山
2003-07-01
学位类型工学博士
中文摘要三维几何造型技术在虚拟现实,游戏及影视制作,文物保护等诸多 领域都有着广泛的应用前景。随着模型真实性要求的提高,以及计算机 技术和激光扫描技术的进步,基于实物测量的数字建模成为三维几何造 型技术发展的一个重要趋势。本文对这种造型技术中的两个关键问题— 曲面重建和简化进行了研究,即:怎样由测量得到的三维坐标建立可 由计算机进行分析、计算和绘制的几何模型;如何根据应用的要求对模 型作适当的简化以达到利于存储、传输与绘制的目的。 现有的重建方法可根据重建曲面是否通过采样点被分为插值与近似 两类。由于采样点位于重建曲面上,插值类算法结果比近似类算法重建 曲面要更为忠实于原始曲面。这类算法一般通过计算几何中的Voronoi 图及对Delauany三角剖分来找到采样点之间的拓扑连接关系。其中, Voronoi滤波算法在满足一定采样条件时可以保证正确地重建出原始曲 面。但是,由于需要计算两次Voronoi图或对偶的Delauany三角剖分, 尤其是在第二次计算时输入点的数量几乎为采样点的三倍,此算法的效 率不高,影响了它在实际中的应用。本文在现有Voronoi滤波算法基础 上,提出一种快速重建方法。在重建之前,根据采样条件,在数据点集 中重新作一次采样,去除一些对正确重建贡献不大的点,使得数据量得 到了很大降低,从而有效地提高了重建速度。实验结果表明,重建曲面 不仅具有正确拓扑结构,同时很好地保持了模型的重要细节特征。 大部分现有网格简化方法都是基于几何元素删除的方法。但是它们 一般用局部误差来描述简化模型与原始模型之间的逼近程度,并用它来 指导模型的简化。一方面,局部误差不能从整体上直观地描述模型的细 节程度,另一方面计算起来也比较复杂。受到曲面重建采样条件的启发, 本文在简化中引入局部特征尺寸,提出了一种新的简化准则,并采用顶 点删除的方法对网格作了简化。对于一个可被删除的顶点,用局部简化 尺度来决定它是否被删除。局部简化尺度由局部特征尺寸和事先给定的 简化细节度共同确定。这样,一方面可以用简化细节度来控制整体简化 程度,另一方面又可以根据模型表面各局部区域细节疏密程度自动做出 相应的调整。具体的简化策略是从某一点出发,删除其相邻点。由于在 简化之前按照局部特征尺寸对顶点作了排序,局部最重要的点被优先保 留了下来。实验结果表明,简化后的模型能够很好地保持模型的视觉特 征,而细节度参数也可以有效而直观地反映出简化
英文摘要3D geometric modeling has numerous applications in the fileds of virtual reality, film and game production, and relic protection. The development in 3D scanning and computer technologies, along with the increasing requirement for the reality of the model, has made the measure based modeling become an important trend in computer graphics. This thesis centers on the issue of surface reconstruction from unorganized points and mesh simplification,which are the two main parts of 3D geometric modeling. The proposed algorithms of surface reconstruction can be divided into two kinds: interpolation and approxmation. The result of the former kind is more faithful to the original surface. Interpolation algorithms usually find the topological relations among the samples through Voronoi diagram and its dual Delaunay triangulation. Among these algorithms, Voronoi filtering can correctly reconstruct the surface from sufficiently dense samples. However, the algorithm is restricted in practical application because it takes too long time to run. In practice, the sampling density required for correct reconstruction is varied in different area: dense in detailed areas and sparse in featurelss ones. Based on this fact, we presented a fast algorithm for surface reconstruction from unorganized points. We non-uniformly sample the input point sets according to sampling condition, so that the amount of points used for reconstruction is greatly decreased and the speed of reconstruction is improved. The results show that the reconstructed surface is topologically correct and the important details are kept very well. The majority of mesh simplification algorithms are based on geometric elements decimation. In these algorithms, the local error not only discribs the approximation between the simplified model and the original model, but also supervises the simplification. The local error cannot globally discrib the details in the model. On the other hand, it is very complex to comput. Enlighten by the sampling condition of surface reconstruction, we introduce the local feature size into simplification and propose a new simplification criterion. In this method, we first compare the distance between a vertex and its neighbor, then delete the neighbor within the local scale and triangulate the hole caused by deletion. Because of the local feature size, the local scale can automatically adjust according to the local area with a gobal precision control. The results show that this method can decrease the amount of data effectively, as well as preserve the vision characters very well.
关键词三维几何建模 曲面重建 采样理论 网格简化 3d Geometric Modeling Surface Reconstruction Sampling Theory Mesh Simplificaiton
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5781
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
高山. 三维几何建模中的曲面重建与简化研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
博士生学位论文-740.pdf(9915KB) 暂不开放CC BY-NC-SA
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[高山]的文章
百度学术
百度学术中相似的文章
[高山]的文章
必应学术
必应学术中相似的文章
[高山]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。