Abstract
The surface reconstruction problem is formulated as a two-stage
reconstruction procedure. The first stage is a robust local fit to the
data in a multiresolution scheme and the second is a regularized least
squares fit, with the addition of an adaptive mechanism in the
smoothness functional in order to make the solution well behaved. The
authors present the details of the second stage in which they use the
weighted bicubic spline as a surface representation in a regularization
framework, with a Tikhonov stabilizer, as the smoothness norm. It is
shown how the adaptive weights, in the stabilizer help the surface bend
across discontinuities by varying the energy of the surface
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