Rotation Synchronization via Deep Matrix Factorization
Gk Tejus Giacomo Zara Paolo Rota Andrea Fusiello Elisa Ricci Federica Arrigoni
In this paper we address the rotation synchronization problem, where the
objective is to recover absolute rotations starting from pairwise ones, where
the unknowns and the measures are represented as nodes and edges of a graph,
respectively. This problem is an essential task for structure from motion and
simultaneous localization and mapping. We focus on the formulation of
synchronization via neural networks, which has only recently begun to be
explored in the literature. Inspired by deep matrix completion, we express
rotation synchronization in terms of matrix factorization with a deep neural
network. Our formulation exhibits implicit regularization properties and, more
importantly, is unsupervised, whereas previous deep approaches are supervised.
Our experiments show that we achieve comparable accuracy to the closest
competitors in most scenes, while working under weaker assumptions.