Papers › Robust Line Segments Matching via Graph Convolution Networks
Robust Line Segments Matching via Graph Convolution Networks
QuanMeng Ma, Guang Jiang, DianZhi Lai
Line matching plays an essential role in structure from motion (SFM) and simultaneous localization and mapping (SLAM), especially in low-textured and repetitive scenes. In this paper, we present a new method of using a graph convolution network to match line segments in a pair of images, and we design a graph-based strategy of matching line segments with relaxing to an optimal transport problem. In contrast to hand-crafted line matching algorithms, our approach learns local line segment descriptor and the matching simultaneously through end-to-end training. The results show our method outperforms the state-of-the-art techniques, and especially, the recall is improved from 45.28% to 70.47% under a similar presicion. The code of our work is available at https://github.com/mameng1/GraphLineMatching.
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