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| In this paper two kinds of bivariate $S$-$lambda$ basis functions, tensor product $S$-$lambda$ basis functions and triangular $S$-$lambda$ basis functions, are constructed by means of the technique of generating functions and transformation factors. These two kinds of bivariate $S$-$lambda$ basis functions have lots of important properties, such as non-negativity, partition of unity, linear independence and so on. The framework of the tensor product $S$-$lambda$ basis functions provides a unified scheme for dealing with several kinds of tensor product basis functions, such as the tensor product Bernstein basis functions, the tensor product Poisson basis functions and some other new tensor product basis functions. The framework of the triangular $S$-$lambda$ surface basis functions includes the triangular Bernstein basis functions, the rational triangular Bernstein basis functions and some other new triangular basis functions. Moreover, the corresponding two kinds of $S$-$lambda$ surfaces are constructed by means of these two kinds of basis functions, respectively. These two kinds of $S$-$lambda$ surface patches have the important properties of surface modeling, such as affine invariance, convex hull property and so on. |
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| Keywords:tensor product $S$-$lambda$ basis functions, triangular $S$-$lambda$ basis functions, generating functions, transformation factors, $S$-$lambda$ surface patches. |
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