$F^2$SLAM: TURNING FEED-FORWARD GEOMETRY INTO PERSISTENT FACTORS FOR SLAM

Feed-forward 3D models provide strong multi-view geometric priors, while online simultaneous localization and mapping (SLAM) relies mainly on local measurements and can accumulate drift over long sequences. Existing attempts to combine the two typically treat feed-forward predictions as an external geometric state that is aligned or fused with the online estimate after the fact, which keeps broader multi-view evidence outside the optimizer that refines the SLAM state. We present $F^{2}SLAM$, which instead converts feed-forward geometry directly into optimization-native target-weight measurements attached to a persistent dense factor graph. A high-frequency stream maintains local tracking constraints and graph connectivity, while a low-frequency stream uses wider multi-view context to selectively refresh existing measurements after a state-consistency check. Both streams constrain the same poses, inverse depths, and optional camera intrinsics through a single dense bundle adjustment. Experiments on multiple benchmarks demonstrate consistently strong trajectory estimation and improved dense reconstruction in both calibrated and uncalibrated settings. Notably, the uncalibrated configuration reduces the average ATE RMSE from $0.030$ m for the strongest feed-forward baseline to $0.002$ m on the Replica dataset.