Line segment matching for wide-baseline images is challenging due to the significant viewpoint differences. In this study, we propose a hierarchical line segment matching method based on viewpoint robust local structure and geometric constraints. In our approach, line segments are paired and classified into three types representing heuristically those with different level of matchability: structured line pairs (S-LPs), unstructured line pairs (U-LPs), and individual line segments (I-LSs). Accordingly, we design a hierarchical matching framework that consists of three matching layers respectively corresponding to the above three types: in the first layer, robust local structures are constructed for S-LPs. We match the S-LPs by measuring local structure similarity (LSS). In the second layer, we build a topological descriptor-based constraint based on the S-LP matches and combine it with epipolar geometry-based constraints to select candidate matches for U-LPs, and then use LSS to further refine the matches. In the third layer, we estimate local homography for I-LSs to build constraints, to extract matches by exploiting the implicit region information of line pair matches. A pair of pre- and postprocessing algorithms, namely line segment merging and match reassignment, are performed before and after the matching procedure to overcome the negative effect of line segment fragmentation on the matching. Experimental results demonstrate that the proposed method performs better than state-of-the-art methods (with the largest improvement of 124.17% in terms of F-Measure over the best one among the compared methods).

由于显着的视点差异，宽基线图像的线段匹配具有挑战性。在这项研究中，我们提出了一种基于视点鲁棒局部结构和几何约束的分层线段匹配方法。在我们的方法中，线段被配对并分为三种类型，代表具有不同匹配性级别的启发式：结构化线对（S-LP）、非结构化线对（U-LP）和单个线段（I-LS）。因此，我们设计了一个分层匹配框架，该框架由三个匹配层组成，分别对应于上述三种类型：在第一层中，为 S-LP 构建了稳健的局部结构。我们通过测量局部结构相似性（LSS）来匹配 S-LP。在第二层，我们基于 S-LP 匹配构建基于拓扑描述符的约束，并将其与基于对极几何的约束相结合，为 U-LP 选择候选匹配，然后使用 LSS 进一步细化匹配。在第三层，我们估计 I-LS 的局部单应性以构建约束，通过利用线对匹配的隐式区域信息来提取匹配。在匹配过程之前和之后执行一对预处理和后处理算法，即线段合并和匹配重新分配，以克服线段碎片对匹配的负面影响。实验结果表明，所提出的方法比最先进的方法表现更好（在 F-Measure 方面比比较方法中最好的方法提高了 124.17%）。