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Zhu L.,The Key Laboratory of Machine Perception and Intelligent | Zhu L.,Peking University | Wang G.,The Key Laboratory of Machine Perception and Intelligent | Wang G.,Peking University
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | Year: 2011

The quality of traditional subdivision curves are restricted by the local mask of subdivision schemes. Notice that a step of subdivision can be decomposed into a sequence of simple stages-calculating the difference, interpolation and reconstruction. A class of global subdivision curves is proposed by manipulating the reconstruction stage. Through a reconstruction involving all the control vertices, the subdivision turns to be global. An improvement on the smoothness and curvature distribution is proved then. Further improvement can be achieved by employing high-order difference and smoother interpolations. Experiments show that the curvature distribution improves under our new construction for subdivision curves. Source


Zhang H.,Peking University | Zhang H.,The Key Laboratory of Machine Perception and Intelligent | Gen B.,Peking University | Gen B.,The Key Laboratory of Machine Perception and Intelligent | And 2 more authors.
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | Year: 2011

Feature detection and extraction plays important role in mesh editing. However, most existing algorithms often fail in dealing with irregular meshes. To overcome those problems, an algorithm for extracting feature edges of triangle meshes based on tensor voting is presented. First, all vertices of an input mesh are classified according to the observation that there is a close correspondence between the eigenvalue distribution of the tensor voting matrix and geometrical features. The classified vertices are then optimized by connecting breakpoints. Region growing is performed for each seed triangle and the boundaries of the regions are extracted as the edges. The experimental results show that the proposed algorithm is effective in nearly all cases, including models with non-uniformly distributed triangles, long and narrow triangles or even holes. It is also robust on noisy data. Source

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