Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment

Robust 3D object detection under adverse weather remains acritical hurdle for autonomous driving. Despite progress with LiDAR–4Dradar fusion, most methods are constrained by a closed-world assump-tion, implicitly requiring training and test weather to align in both typeand severity. This premise fails in practice: the open-ended nature ofweather, and even variations within a single type like rain, cause dramat-ically different LiDAR degradation patterns, leading to significant per-formance drops in unseen conditions. To address this, we present Dual-Critic Guided Diffusion Alignment (DCDA), a weather-agnostic frame-work that learns to recover degraded LiDAR features toward a cleanmanifold. Rather than modeling specific weather types, DCDA employsa 4D radar-conditioned diffusion process to progressively refine features,guided by two complementary critics. (i) A detection-guided critic, an-chored by a pre-trained clean-weather model, ensures that the refinedfeatures retain object-level discriminability and localization accuracy. (ii)A weather adversarial critic enforces holistic distributional consistencywith clean-weather representations. By aligning features through seman-tic and distributional constraints rather than explicit weather model-ing, DCDA generalizes effectively to unseen weather types and severitieswithout requiring paired data or weather labels. We further introduce astructured open-weather benchmark with held-out type–severity combi-nations and extensive experiments verify DCDA’s advantages. Code isavailable at https://github.com/Mangonn/DCDA.