Approximation of Unorganized Point Set with Composite Implicit Surface

We propose a new approach for surface reconstruction from any unorganized point set, with a fixed amount of parameters. First, we consider some local approximations by compactly supported radial basis functions (CSRBFs), then blend them by a partition of unity method (PU). In our scheme, we start by selecting CSRBFs centers, then construct partitions, finally parameters are calculated in order to minimize, on each sub-domain, the mean square error with a regularization constraint. We demonstrate the effectiveness of our approach on several models with different points distributions.