Since feature matching of image pairs brings a heavy computational burden, Structure-from-Motion faces great challenges in efficiency, especially for unordered large-scale image collections. To solve it, we propose a hierarchical image matching method in this paper. Our approach starts with an iterative image retrieval scheme, which can efficiently find potentially overlapping image pairs as candidates and avoid unnecessary computation. Then, feature extraction, feature matching and geometric verification are implemented in candidates to find the verified image pairs and inlier feature correspondences. Experiments on benchmark datasets and large-scale unordered datasets demonstrate that our method performs competitiveness in efficiency, without degrading the accuracy, compared with the state-of-the-art system.
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