Graph-based consistent matching for structure-from-motion

Abstract

Pairwise image matching of unordered image collections greatly affects the efficiency and accuracy of Structure-from-Motion (SfM). Insufficient match pairs may result in disconnected structures or incomplete components, while costly redundant pairs containing erroneous ones may lead to folded and superimposed structures. This paper presents a graph-based image matching method that tackles the issues of completeness, efficiency and consistency in a unified framework. Our approach starts by chaining all but singleton images using a visualsimilarity- based minimum spanning tree. Then the minimum spanning tree is incrementally expanded to form locally consistent strong triplets. Finally, a global community-based graph algorithm is introduced to strengthen the global consistency by reinforcing potentially large connected components. We demonstrate the superior performance of our method in terms of accuracy and efficiency on both benchmark and Internet datasets. Our method also performs remarkably well on the challenging datasets of highly ambiguous and duplicated scenes.