# Learned Gauss-Newton Gauge Solver

Movable multi-camera rigs ship pre-calibrated, but repositioning, thermal cycling and vibration make that calibration go stale, and classical bundle adjustment needs a fresh multi-view capture plus a global optimization whose cost grows with the rig, ruling out real-time use. RECALL anchors on the factory calibration and, from a brief observation of a known object, predicts in a single feed-forward pass a shared global rotation of the rig plus small per-camera drift, solving the shared rotational gauge in-network with a learned Gauss-Newton step. On a synthetic 10-camera benchmark its parameter errors are lower than per-camera and rig-coupled joint bundle adjustment, at 0.12 ms per rig.

Movable multi-camera rigs ship pre-calibrated, but repositioning, thermal cycling and vibration make that calibration go stale, and classical bundle adjustment needs a fresh multi-view capture plus a global optimization whose cost grows with the rig, ruling out real-time use. RECALL anchors on the factory calibration and, from a brief observation of a known object, predicts in a single feed-forward pass a shared global rotation of the rig plus small per-camera drift, solving the shared rotational gauge in-network with a learned Gauss-Newton step. On a synthetic 10-camera benchmark its parameter errors are lower than per-camera and rig-coupled joint bundle adjustment, at 0.12 ms per rig.