Distributable Consistent Multi-Object Matching
This addresses the scalability issue in multi-object matching for large-scale datasets, though it appears incremental as it builds on existing optimization-based frameworks.
The paper tackles the problem of ensuring consistent maps across multiple objects by dividing the object collection into overlapping sub-collections and enforcing consistency within each, resulting in a scalable distributed framework that is competitive with state-of-the-art methods on synthetic and real-world datasets.
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.