ReCall-SLAM: Long-Horizon RGB-D SLAM through Recurring Local Reconstruction

Depth provides RGB-D SLAM with explicit geometry and metric scale, yet brief blur, texturelessness, weak overlap, or corrupted depth can still break an otherwise long trajectory. This exposes a weakness in local multi-view geometry. More broadly, this local capability is required not only by the frontend but throughout SLAM. RGB-D SLAM can be viewed as repeatedly solving the same local problem at increasing map scales: nearby views recover camera motion and local structure, wider observations reconcile geometry over a region, and views around a place revisited much later relate distant parts of the trajectory. Each task jointly recovers cameras and scene structure from a bounded set of spatially related RGB-D observations, then applies the estimate at the appropriate map scale. This suggests that a long-horizon system should make a strong local solver available across its hierarchy. Based on this insight, we present ReCall-SLAM, a training-free system that retains observation history and repeatedly poses these bounded reconstruction problems to one pretrained Pi3X model. Every model call natively consumes RGB, calibrated depth, and camera intrinsics and predicts cameras, dense geometry, and confidence. Bounded regional graphs and a compact global graph integrate these predictions into a persistent, revisable map while all levels advance asynchronously. Experiments demonstrate stable, coherent reconstruction on sequences extending to tens of thousands of frames; ReCall-SLAM also improves the FastCaMo-Real F-score by 20.5 percentage points and reduces Oxford Spires ATE by 58.3%. Project page: https://anonymous-submission-x.github.io/ReCallSLAM/.