Estimation of Differential Properties on Point-sampled Surfaces and Its Applications
点模型微分属性的估算及其应用

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.