TY - JOUR
AU - Brunet, Florent
AU - Bartoli, Adrien
AU - Malgouyres, Rémy
AU - Navab, Nassir
T1 - L-Tangent Norm: A Low Computational Cost Criterion for Choosing Regularization Weights and its Use for Range Surface Reconstruction
JO - Proceedings of 3D Data Processing, Visualization and Transmission
Y1 - 2008
N2 - 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.
ER -