L-Tangent Norm: A Low Computational Cost Criterion for Choosing Regularization Weights and its Use for Range Surface Reconstruction

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.     «

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, t...     »