The Shortest Matching Path Based On Novel Cycle Consistency

Category-level image matching is extremely challenging due to various intra-class variations. To tackle the large variations, we propose an algorithm to jointly estimate the dense correspondence for image set, which reformulates image set alignment into the problem of shortest path searching. We propose a novel tri-image cycle-consistency to measure the matching "distance" between two image, which is further used to improve the pair-wise dense correspondence. Meanwhile, we utilize CNN feature pyramid to achieve pair-wise image matching hierarchically. Extensive experiments and analysis demonstrate the superiority of our method in matching images with challenging variations.