UniSim-SLAM: Feed-Forward SLAM with Unified Sim(3) Optimization

Recent geometric foundation models enable feed-forward in-ference for SLAM, but their predictions are strongly dependent on the in-put view set, which leads to geometric inconsistencies and trajectory driftwhen results are chained over long sequences. Online deployment furtherexposes a trade-off between the low latency of two-view tracking and theconstraint richness of multi-view inference. We introduce UniSim-SLAM,an integrated system that runs lightweight two-view keyframe trackingin the frontend and performs periodic multi-view submap refinementin the backend. To combine predictions defined in heterogeneous localcoordinates with inconsistent scales, we formulate a unified multi-levelfactor graph on Sim(3) that jointly optimizes global keyframe poses andsubmap poses. The graph integrates temporal view-to-view odometryedges, view-to-submap bridge edges with depth-statistics scale anchor-ing, and submap-to-submap tie and scale constraints to enforce consis-tent similarity relations across submaps. Experiments on TUM RGB-Dand 7-Scenes show that UniSim-SLAM achieves state-of-the-art accuracyin the uncalibrated setting, reducing trajectory error by 38.5% on TUMRGB-D and 45.9% on 7-Scenes compared to prior best results. Projectpage: https://vision3d-lab.github.io/unisim-slam/.