Robust Line Segments Matching via Graph Convolution Networks
This addresses a bottleneck in computer vision for low-textured and repetitive scenes, offering a significant performance boost over existing methods.
The paper tackles the problem of matching line segments in images for structure from motion and SLAM, particularly in challenging scenes, by introducing a graph convolution network method that learns descriptors and matching end-to-end, achieving a recall improvement from 45.28% to 70.47% at similar precision.
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.