DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection

Stereo-based 3D obstacle perception for autonomous driv-ing is currently constrained by an imbalanced triplet: deployment cost,detection accuracy, and open-set adaptability. While existing methodsstruggle to balance these three competing objectives, there is an urgentdemand for high-precision, real-time algorithms capable of detecting ar-bitrary obstacles in the wild. In this paper, we present DDStereo, a novelDual-Decoder Stereo Transformer that achieves a synergistic integrationof 3D object detection and Out-of-Distribution (OoD) road anomaly de-tection. Leveraging the geometric priors of stereo disparity, our approacheffectively couples 3D attribute regression with open-set foreground de-tection within a streamlined dual-branch decoder architecture. Conven-tional methods rely on complex feature-level fusion; DDStereo main-tains execution efficiency by employing a decoupled decoding strategyand shared object-level queries to ensure cross-modal target alignment.Extensive evaluations of public benchmarks demonstrate that DDStereonot only achieves state-of-the-art accuracy under open-set and closed-set protocols. Our method delivers real-time performance comparable tomonocular 3D detection baselines, providing a cost-effective solution forthe perception of obstacles of the normal and OoD category. Code andmodels are available at https://github.com/shiyi-mu/DDStereo.