In this paper we propose an optimization-based framework to multiple object matching. The framework takes maps computed between pairs of objects as input, and outputs maps that are consistent among all pairs of objects. The central idea of our approach is to divide the input object collection into overlapping sub-collections and enforce map consistency among each sub-collection. This leads to a distributed formulation, which is scalable to large-scale datasets. We also present an equivalence condition between this decoupled scheme and the original scheme. Experiments on both synthetic and real-world datasets show that our framework is competitive against state-of-the-art multi-object matching techniques.
@article{arxiv.1611.07191,
title = {Distributable Consistent Multi-Object Matching},
author = {Nan Hu and Qixing Huang and Boris Thibert and Leonidas Guibas},
journal= {arXiv preprint arXiv:1611.07191},
year = {2018}
}