论文标题
强大的线段通过图卷积网络匹配
Robust Line Segments Matching via Graph Convolution Networks
论文作者
论文摘要
匹配线在运动（SFM）和同时定位和映射（SLAM）的结构中起着至关重要的作用，尤其是在低纹理和重复的场景中。在本文中，我们提出了一种使用图形卷积网络来匹配一对图像中的线段的新方法，并设计了一种基于图形的策略，以匹配线段，并放松到最佳的传输问题。与手工制作的线路匹配算法相反，我们的方法通过端到端培训同时学习本地线段描述符和匹配。结果表明，我们的方法的表现优于最新技术，尤其是在类似的主机下，召回率从45.28％提高到70.47％。我们的工作代码可从https://github.com/mameng1/graphlinematching获得。
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.
