Feature Matching via Graph Clustering with Local Affine Consensus

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.