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题名:
line drawings abstraction from 3d models
作者: Zhao Shujie ; Wu Enhua
关键词: Digital storage
刊名: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
发表日期: 2011
卷: 6530, 期:1, 页:104-111
收录类别: EI
部门归属: (1) Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, China; (2) State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
英文摘要: Non-photorealistic rendering, also called stylistic rendering, emphasizes on expressing special features and omitting extraneous information to generate a new scene different from the primary one through digital processing. The stylistic rendering is of importance in various applications, in particular in the entertainment such as production of cartoon, digital media for mobiles etc. Line drawing is one of the rendering techniques in non-photorealistic rendering. Using feature lines to convey salient and important aspects of a scene while rendering could provide clearer ideas for model representation. In this regard, we propose a method to extract feature lines directly from three-dimensional models in this paper. By the method, linear feature lines are extracted through finding intersections of two implicit functions that can work without lighting, and rendered with visibility in a comprehensive way. Starting from an introduction to the purpose of line drawings, the development of the method is described in this paper. An algorithm for line extraction using implicit functions is presented in the main part of the paper. Test results and analysis on performance of the test are given. Finally, a conclusion is made, and the future development on line drawings is discussed. © 2011 Springer-Verlag Berlin Heidelberg.
内容类型: 期刊论文
URI标识: http://ir.iscas.ac.cn/handle/311060/14025
Appears in Collections:计算机科学国家重点实验室 _期刊论文

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Recommended Citation:
Zhao Shujie,Wu Enhua. line drawings abstraction from 3d models[J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2011-01-01,6530(1):104-111.
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