Ricerc@Sapienza

We introduce a novel computational framework for digital geometry processing, based upon the derivation of a nonlinear operator associated to the total variation functional. Such an operator admits a generalized notion of spectral decomposition, yielding a convenient multiscale representation akin to Laplacian-based methods, while at the same time avoiding undesirable over-smoothing effects typical of such techniques.

We introduce a new framework for learning dense correspondence between deformable geometric domains such as polygonal meshes and point clouds. Existing learning based approaches model correspondence as a labelling problem, where each point of a query domain receives a label identifying a point on some reference domain; the correspondence is then constructed a posteriori by composing the label predictions of two input geometries.