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A novel idea for perception of object surfaces is presented by so called "shape descriptors". Such idea is as an abstract level to represent the object surface by some real numbers. It has the similar idea like as the Fourier coefficients of mapping a function f(x) to frequency domain by Fourier transform. The main goal of this thesis is to define some of the key issues in understanding of an object shape and also to find a modeling methodology to create the "shape descriptors". The modeling methodology is designed based on a variational interpolation technique. Such technique is used to generate a group of variational implicit functions with help of radial basis functions. In our modeling methodology, we randomly choose some reference points on a set of related concentric spheres around a 3D point cloud data as known values in variational implicit functions. The "shape descriptors" are found from these implicit functions implementing LU decomposition. We show that the "shape descriptors" are invariant to size and positioning (rotation and translation) changes of a shape and they are also effective tools for matching of two similar objects surfaces.