  Despite the success of deep functional maps in non-rigid 3D shape matching,
there exists no learning framework that models both self-symmetry and shape
matching simultaneously. This is despite the fact that errors due to symmetry
mismatch are a major challenge in non-rigid shape matching. In this paper, we
propose a novel framework that simultaneously learns both self symmetry as well
as a pairwise map between a pair of shapes. Our key idea is to couple a self
symmetry map and a pairwise map through a regularization term that provides a
joint constraint on both of them, thereby, leading to more accurate maps. We
validate our method on several benchmarks where it outperforms many competitive
baselines on both tasks.
