Calibrate What You Trust: Reliable Multimodal 3D Panoptic Segmentation under Domain Shift

We present the first study of Domain Generalization (DG) for multimodal 3D panoptic segmentation (mm-3DPS), targeting robust perception under unseen domain shifts in autonomous driving. We identify two key challenges: reliable multimodal representation learning when individual modalities are affected differently by domain shifts, and reliable panoptic prediction when these shifts propagate to the final scene decomposition. To address them, we propose RAMP3D, a DG framework specifically designed for mm-3DPS. RAMP3D incorporates Reliability-guided Multimodal Adaptation (ReMA), which explicitly estimates modality reliability and adaptively regulates multimodal adaptation under domain shift. It further introduces Reference-based Calibration (ReCal), which leverages stable scene-relative cues to separately calibrate stuff-region predictions and thing-instance scores. Extensive experiments across diverse shifts in time, weather, location, and sensor conditions show that RAMP3D consistently outperforms strong baselines, including adaptations of state-of-the-art 3D DG and multimodal UDA methods.