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采用稀疏矩阵向量乘法的细分曲面求值法

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

作者:Michael Driscoll 作者单位:EECS Department University of California, Berkeley 加工时间:2015-03-22 信息来源:EECS 索取原文[16 页]
关键词:细分曲面求值;稀疏矩阵;细分矩阵;矩阵乘法;向量
摘 要:We present an interpretation of subdivision surface evaluation in the language of linear algebra. Specifically, the vector of surface points can be computed by left-multiplying the vector of control points by a sparse subdivision matrix. This “matrix-driven” interpretation applies to any level of subdivision, holds for many common subdivision schemes (including Catmull-Clark and Loop), supports limit surface evaluation, allows semi-sharp creases, and complements feature-adaptive optimizations. It is primarily applicable to static meshes undergoing deformation (i.e. animation), in which case the subdivision matrix is invariant over time and the surface can be evaluated at each frame with a single sparse matrix-vector multiplication (SpMV).
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