Iterative mutual voting matching for efficient and accurate Structure-from-Motion

As a crucial topic in 3D vision, Structure-from-Motion (SfM) aims to recover camera poses and 3D structures from unconstrained images. Performing pairwise image matching is a critical step. Typically, matching relationships are represented as a view graph, but the initial graph often contains redundant or potentially false edges, affecting both efficiency and accuracy. We propose an efficient incremental SfM method that optimizes the critical image matching step. Specifically, given an image similarity graph, an initialized weighted view graph is constructed. Next, the vertices and edges of the graph are treated as candidates and voters, with iterative mutual voting performed to score image pairs until convergence. Then, the optimal subgraph is extracted using the maximum spanning tree (MST). Finally, incremental reconstruction is carried out based on the selected images. We demonstrate the efficiency and accuracy of our method on general datasets and ambiguous datasets.