Practical and Efficient Multi-View Matching

ICCV17 | 1260 | Practical and Efficient Multi-View Matching
Eleonora Maset (University of Udine), Federica Arrigoni (University of Udine), Andrea Fusiello (University of Udine)
In this paper we propose a novel solution to the multi-view matching problem that, given a set of noisy pairwise correspondences, jointly updates them so as to maximize their consistency. Our method is based on a spectral decomposition, resulting in a closed-form efficient algorithm, in contrast to other iterative techniques that can be found in the literature. Experiments on both synthetic and real datasets show that our method achieves comparable or superior accuracy to state-of-the-art algorithms in significantly less time. We also demonstrate that our solution can efficiently handle datasets of hundreds of images, which is unprecedented in the literature.