Wavelet discretization methods for surface estimation and reconstruction

Orthonormal wavelet bases recently constructed by Ingrid Daubechies provide an efficient means of discretely representing curves and surfaces for computer vision and graphics. Advantages of this representation implied by the scaling, finite extent, and vanishing moment properties of the basis functions include multi-level algorithms, spatially adaptive resolution, and high order approximations with respect to Sobelev norms. This paper reviews the construction of these wavelet bases and describes wavelet discretization methods. It discusses two specific applications to surface estimation and reconstruction and presents preliminary numerical results.