Estimation of Differential Properties on Point-sampled Surfaces and Its Applications
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摘要: 为了有效地估算点模型的微分属性,提出了一种基于几何特征相似性的估算方法. 首先,利用Mean shift (MS)聚类法,对点模型进行几何特征相似性聚类;然后,基于径向基函数(Radial basis functions, RBF),重构各聚类单元的局部隐式曲面; 最后,依据经典微分几何理论,在径向基函数 曲面上便捷地求解采样点的微分属性并给出具体应用. 实验与应用结果表明,该方法能够比较精确地估算出点模型的微分属性且得到有效应用.Abstract: Based on the geometry-features similarity, an algorithm is presented for effectively estimating the differential properties on point-sampled surfaces (PSS). By using mean-shift (MS) clustering, PSS is first clustered into clusters according to the surface-features similarity. Based on radial base functions, a local implicit surface is then reconstructed that approximates the sampled points in a cluster. By applying the theory of classical differential geometry to each implicit surface, the differential properties of each sampled point on PSS are finally estimated and their applications are given. Some experimental results demonstrate that the algorithm can accurately estimate the differential properties on PSS and is effective.
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