  We provide a novel new approach for aligning geometric models using a dual
graph structure where local features are mapping probabilities. Alignment of
non-rigid structures is one of the most challenging computer vision tasks due
to the high number of unknowns needed to model the correspondence. We have seen
a leap forward using DNN models in template alignment and functional maps, but
those methods fail for inter-class alignment where nonisometric deformations
exist. Here we propose to rethink this task and use unrolling concepts on a
dual graph structure - one for a forward map and one for a backward map, where
the features are pulled back matching probabilities from the target into the
source. We report state of the art results on stretchable domains alignment in
a rapid and stable solution for meshes and cloud of points.
