Distributable Consistent Multi-Object Matching

TL;DR

This paper introduces a scalable, optimization-based framework for multi-object matching that ensures consistency across all object pairs by dividing data into overlapping sub-collections, demonstrated to be effective on synthetic and real datasets.

Contribution

It presents a novel distributed approach for multi-object matching that maintains global consistency and is scalable to large datasets, with an established equivalence condition.

Findings

Framework is competitive with state-of-the-art methods

Distributed formulation improves scalability

Effective on both synthetic and real-world datasets

Abstract

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.