%0 %0 Journal Article %A Brunet, Florent; Bartoli, Adrien; Malgouyres, Rémy & Navab, Nassir %D 2008 %T L-Tangent Norm: A Low Computational Cost Criterion for Choosing Regularization Weights and its Use for Range Surface Reconstruction %E %B Proceedings of 3D Data Processing, Visualization and Transmission %C %I %V %6 %N %P %& %Y %S %7 %8 %9 %? %! %Z %@ %( %) %* %L %M %2 %3 article %4 %# %$ %F brunet2008ltn %K %X We are interested in fitting a surface model such as a tensor-product spline to range image data. This is commonly done by finding control points which minimize a compound cost including the goodness of fit and a regularizer, balanced by a regularization parameter. Many approaches choose this parameter as the minimizer of, for example, the cross-validation score or the L-curve criterion. Most of these criteria are expensive to compute and difficult to minimize. We propose a novel criterion, the L-tangent norm, which overcomes these drawbacks. It gives sensible results with a much lower computational cost. This new criterion has been successfully tested with synthetic and real range image data. %Z %+ %^