DOME: Distortion-Driven Domain Mixture Experts for All-Weather Multimodal 3D Object Detection

Reliable 3D perception under adverse weather is essential for autonomous driving, yet remains challenging due to weather-induced sensor degradation. Different adverse weather conditions introduce heterogeneous physical distortions into LiDAR observations, leading to latent domain shifts that are difficult to characterize with discrete weather categories. Existing weather-aware approaches typically rely on image-based weather classifiers to route features according to predefined weather labels, resulting in a mismatch between semantic weather categories and actual sensor degradation patterns, especially under degraded visual conditions. To address this challenge, we propose DOME, a Distortion-Driven Domain Mixture-of-Experts framework for all-weather multimodal 3D object detection. DOME models LiDAR–4D Radar fusion through latent physical-distortion domain decomposition, where experts are specialized according to distortion patterns rather than predefined weather identities. Specifically, the Physical Distortion-guided Router (PDR) extracts distortion fingerprints from multimodal features and guides Distortion-Specialized Experts (DSE) to perform distortion-aware feature adaptation. Furthermore, we introduce DOME-L with LoRA-based Distortion Experts (LDE), reducing the parameter overhead of distortion-driven MoE while preserving domain-specific adaptation capability. Extensive experiments on K-Radar and OmniHD-Scenes demonstrate strong performance. On K-Radar, DOME improves L4DR by 8.1 $A{P}_{3D}$ points overall, while a capacity-controlled comparison isolates a 1.9-point gain from distortion-aware routing. Additional evaluations demonstrate robustness under continuously varying and held-out degradation conditions, as well as transferability to a different multimodal detection backbone. The open-source code will be publicly available.