Could you give more explanation about the model bias in this statement " However, sometimes the surfaces are perfectly fine, but the registration does not perfectly match the surface, due to model bias. In this case, a projection can help to improve the correspondence."
Has it (model bias) something to do with the analytic kernel used for the registration (e.g. a multiscale kernel) ?
By comparing visually in orthogonal view, I found that the fitted mesh is shrinked a little bit compared to the projected mesh (and the target one) and I have an average distance of 1.50 mm and a Hausdorff distance of 6 mm between the target mesh and the fitted one. The overall shape is almost the same when dispalying in 3D.
In fact, my main question is "how good should be the fitted meshes before building the model by PCA ?" When do I stop improving the registration ? I followed the decreasing regularized weights strategy as in Tutorial 12. Is it mandatory that the fitted mesh and the projected one fit together ?
Generally, did you found that the parametric non-rigid registration performed well compared to the non rigid ICP ?