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.

翻译：本文研究了旋转同步问题，其目标是从成对旋转中恢复绝对旋转，其中未知量和测量值分别表示为图中的节点和边。该问题是运动恢复结构和同时定位与地图构建中的关键任务。我们聚焦于通过神经网络进行同步的建模方法，这一方向在文献中近期才被探索。受深度矩阵补全的启发，我们将旋转同步表示为基于深度神经网络的矩阵分解形式。我们的方法展现出隐式正则化特性，更重要的是，它是无监督的，而以往的深度方法均为监督式。实验表明，在大多数场景下，我们在更弱假设下能达到与最接近的竞争者相当的精度。