Unpaired Geometry-Guided Sim2Real Translation for Autonomous Driving

Simulation is indispensable for autonomous driving, yet thepronounced visual Sim2Real gap severely limits the cross-domain gen-eralization of perception models. To bridge this gap, we propose Cross-Domain Control Transfer (CDCT), a diffusion-based Sim2Real frame-work for translating synthetic images into realistic counterparts withoutpaired supervision. CDCT features a novel cross-domain score composi-tion mechanism that injects domain-agnostic geometry guidance, derivedfrom synthetic-domain score differences, into a real-world appearanceprior. This ensures strict geometric and semantic consistency withoutpaired real-world spatial conditions. However, naively incorporating mul-tiple geometric conditions often incurs a prohibitive parameter overhead.To address this, our Lightweight Multi-Condition Adapter (LMCA) mod-ule processes diverse rendering buffers simultaneously, eliminating thecomputational redundancy of standard multi-branch architectures. Ex-tensive experiments on the CARLA-to-Cityscapes benchmark demon-strate that CDCT yields substantial zero-shot performance gains ondownstream perception tasks. Crucially, joint training with our generateddata surpasses the performance of real-world-only training, showcasingits immense potential for autonomous driving.