RECALL: Real-Time Recalibration of Multi-Camera Rigs via Learned OEM-Anchored Residuals

Multi-camera rigs ship pre-calibrated from the manufacturer, but their calibration goes stale each time the rig is repositioned, and drifts continuously through wear, thermal cycling, and vibration. Recovering it on-the-fly is hard: classical bundle adjustment needs a fresh multi-view capture and a global optimization whose cost grows with the rig, ruling out real-time use. In this paper we present RECALL, a feed-forward method that recalibrates a movable multi-camera rig in a single pass. From the nominal OEM calibration as an anchor and a brief observation of a known object, it predicts each camera's current intrinsics and extrinsics, written as a shared global rotation of the rig composed with a small per-camera drift. Instead of regressing the calibration in one shot, RECALL refines its estimate with a learned, rig-coupled recurrent operator that reprojects through a differentiable projection and applies attention across the cameras at each step, and solves for the shared rotational gauge in-network with a learned Gauss-Newton step rather than regressing it. Because the ground truth is synthesizable, we train entirely on data generated on-the-fly and need no captured dataset. On a synthetic 10-camera benchmark, RECALL is more accurate than per-camera and rig-coupled joint bundle adjustment (0.26^\circ vs 0.47^\circ rotation, $5.4$ vs $8.9$ px focal) and runs about 10^5× faster at about $0.12$ ms per rig. On real data (TotalCapture, CMU Panoptic) it matches or beats offline rig-coupled bundle adjustment on absolute and relative rotation while running four-to-five orders of magnitude faster, and on the Panoptic surround rig it also matches the strongest image-only foundation models (VGGT, VGGT-$Ω$, π^3) on relative pose while staying metric and about 15× more accurate in focal length. Code: https://github.com/theICTlab/RECALL-multicam