Orthogonal least square RBF based implicit surface reconstruction methods

Two contributions on 3D implicit surface reconstruction from scattered points are presented in this paper. Firstly, least square radial basis functions (LS RBF) are deduced from the conventional RBF formulations, which makes it possible to use fewer centers when reconstruction. Then we use orthogonal least square (OLS) method to select significant centers from large and dense point data sets. From the selected centers, an implicit continuous function is constructed efficiently. This scheme can overcome the problem of numerical ill-conditioning of coefficient matrix and over-fitting. Experimental results show that our two methods are efficient and highly satisfactory in perception and quantification.