This paper studies graph clustering with application to feature matching and proposes an effective method, termed as GC-LAC, that can establish reliable feature correspondences and simultaneously discover all potential visual patterns. In particular, we regard each putative match as a node and encode the geometric relationships into edges where a visual pattern sharing similar motion behaviors corresponds to a strongly connected subgraph. In this setting, it is natural to formulate the feature matching task as a graph clustering problem. To construct a geometric meaningful graph, based on the best practices, we adopt a local affine strategy. By investigating the motion coherence prior, we further propose an efficient and deterministic geometric solver (MCDG) to extract the local geometric information that helps construct the graph. The graph is sparse and general for various image transformations. Subsequently, a novel robust graph clustering algorithm (D2SCAN) is introduced, which defines the notion of density-reachable on the graph by replicator dynamics optimization. Extensive experiments focusing on both the local and the whole of our GC-LAC with various practical vision tasks including relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multimodel fitting, demonstrate that our GC-LAC is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code for this work is publicly available at: https://github.com/YifanLu2000/GCLAC.

本文研究了图聚类及其在特征匹配的应用，并提出了一种称为 GC-LAC 的有效方法，该方法可以建立可靠的特征对应关系并同时发现所有潜在的视觉模式。特别是，我们将每个假定的匹配视为一个节点，并将几何关系编码为边，其中具有相似运动行为的视觉模式对应于强连接的子图。在这种设置下，很自然地将特征匹配任务表述为图聚类问题。为了构建一个几何有意义的图，基于最佳实践，我们采用了局部仿射策略。通过研究运动相干性先验，我们进一步提出了一种高效的确定性几何求解器 （MCDG） 来提取有助于构建图形的局部几何信息。该图是稀疏的，并且适用于各种图像转换。随后，引入了一种新的鲁棒图聚类算法 （D2SCAN），该算法定义了通过复制器动力学优化在图上可达到密度的概念。专注于局部和整个 GC-LAC 的广泛实验，包括相对位姿估计、单应性和基本矩阵估计、闭环检测和多模型拟合，表明我们的 GC-LAC 在通用性、效率和有效性方面比当前最先进的方法更具竞争力。这项工作的源代码可在以下网址公开获得：https://github.com/YifanLu2000/GCLAC。