On mesh editing, Manifold learning, and diffusion wavelets

Abstract

We spell out a formal equivalence between the naive Laplacian editing and semi-supervised learning by bi-Laplacian Regularized Least Squares. This allows us to write the solution to Laplacian mesh editing in a 'closed' form, based on which we introduce the Generalized Linear Editing (GLE). GLE has both naive Laplacian editing and gradient based editing as special cases. GLE allows using diffusion wavelets for mesh editing. We present preliminary experiments, and shortly discuss connections to segmentation. © 2009 Springer Berlin Heidelberg.