摘要
This paper introduces a robust translation synchronization approach which takes relative directions between pairs of images as inputs and outputs absolute image translations. Our approach is based on a generalized eigenvalue problem, where the formulation contains edge weights in relative directions and vertex weights in absolute image translations. We present a rigorous stability analysis to determine how to set these weights optimally. Specifically, optimal vertex weights are always $1$, while optimal edge weights depend on magnitudes of relative transformations and variances of relative directions. These results lead to an iterative translation synchronization formulation, which progressively removes outliers in the inputs by adaptively adjusting the edge weights. We present exact and robust recovery conditions for our approach under a standard noise model. Experimental results justify our theoretical results and show that our approach outperforms state-of-the-art baseline approaches on both synthetic and real datasets.