TempCalib: Diagnosing and Recovering Extrinsic Drift with a Frozen 3D Detector

Multi-camera 3D detectors rely on accurate extrinsics to align cross-view observations, yet post-deployment drift can degrade downstream perception. Existing solutions either use dedicated online calibration or improve robustness without explicitly restoring corrupted extrinsics. The former adds a separate estimation procedure, while the latter leaves geometric errors unresolved. We investigate whether the deployed detector itself contains evidence for diagnosis and recovery. We propose TempCalib, a framework for unknown-camera fault diagnosis, localization, and rotational recovery using a frozen multi-camera 3D detector. It extracts reusable detector-derived evidence, detects persistent cross-view inconsistency on a camera graph, and generates target-excluded rotational proposals after localization. Cross-view feature consistency and fixed-correspondence epipolar geometry refine these proposals, and final states are compared on common visible support. Fixed clean references calibrate these signals; neither additional training nor per-candidate backbone or decoder passes are required. On a nuScenes-based benchmark, TempCalib detects and localizes every injected fault across three splits sharing a 96-condition single-camera rotational perturbation grid. On blind Test-A, it achieves a mean rotational residual of $0.988^{\circ }$ under the matched 40-sequence setting, reduced to $0.894^{\circ }$ with the extended recovery budget. On the two validation splits, the L4 configuration restores approximately 91–97% of the condition-level mAP/NDS loss. These results show that a frozen detector can provide evidence for explicit geometric recovery and restoration of its own perception.