MVLoMa: Multi-View Local Feature Matching with Any Geometry Foundation Model

Local feature matching is typically formulated for image pairs, although structure-from-motion and visual localization commonly match one reference image against many retrieved or covisible neighbours. Applying a pairwise matcher independently to these centre–neighbour edges duplicates reference-side computation and prevents the matcher from exploiting complementary multi-view geometric evidence from the remaining observations. To address these limitations, we introduce MVLoMa, a sparse star-graph matcher that extends LoMa from image pairs to multiple views. MVLoMa first uses any frozen multi-view geometry model to jointly describe all images in a star, then injects the resulting cross-view geometric context into local descriptors. It then matches the centre image to all neighbours in a single forward pass through per-view self-attention and bidirectional centre–neighbour cross-attention. For supervision, we derive positive correspondences only from depth-valid, mutually visible dense warps and explicitly penalize high-confidence matches that violate epipolar geometry. On 25-view star graphs from HyperSim, ScanNet++, Mip-NeRF 360, and Tanks and Temples, MVLoMa achieves state-of-the-art average relative-pose AUC@$1^{\circ }$ across the four benchmarks. At 25 views, its matcher is $2.20\times$ faster than sequential evaluation of the 24 centre–neighbour edges. Furthermore, MVLoMa can be instantiated with any frozen geometry foundation model—including DA3, ${\pi }^3$, VGGT, and VGGT-$\Omega$—and all variants achieve strong performance. Controlled backbone, supervision, and view-count ablations demonstrate the benefits of geometry-aware descriptors, visibility-aware supervision, geometric negative supervision, and joint multi-view inference.