Efficient Covisibility-based Image Matching for Large-Scale SfM

Obtaining accurate and sufficient feature matches is crucial for robust large-scale Structure-from-Motion. For unordered image collections, a traditional feature matching method with geometric verification requires a huge cost to find sufficient feature matches. Although several methods have been proposed to speed up this stage, none of them makes full use of existing matches. In this paper, we propose a novel efficient image matching method by using the transitivity of region covisibility. The overlapping image pairs can be efficiently found in an iterative matching strategy even only with few inlier feauture matches. The experimental results on unordered image datasets demonstrate that the proposed method is three times faster than the state-of-the-art and the matching result is high-quality enough for robust SfM.