FD-DB: 用于无配对合成到真实域翻译的频率解耦双分支网络
FD-DB: Frequency-Decoupled Dual-Branch Network for Unpaired Synthetic-to-Real Domain Translation
AI总结：
FD-DB通过频率解耦双分支模型提升合成到真实域翻译的稳定性和真实性，优化外观一致性和下游任务性能。
摘要：
合成数据为几何敏感视觉任务提供了低成本、准确标注的样本，但合成域与真实域之间的外观和成像差异会导致严重的域偏移并降低下游性能。无配对的合成到真实域翻译可以在没有配对监督的情况下减少这一差距，但现有方法常面临真实感与结构稳定性之间的权衡：无约束生成可能引入变形或虚假纹理，而过于严格的约束限制了对真实域统计的适应。我们提出了FD-DB，一种频率解耦的双分支模型，将外观传输分为低频可解释编辑和高频残差补偿。可解释分支预测具有物理意义的编辑参数（白平衡、曝光、对比度、饱和度、模糊和颗粒）以构建具有强内容保留的稳定低频外观基础。自由分支通过残差生成补充细节，门控融合机制在显式频率约束下结合两个分支以限制低频漂移。我们进一步采用两阶段训练计划，首先稳定编辑分支，然后释放残差分支以提高优化稳定性。在YCB-V数据集上的实验表明，FD-DB提高了真实域外观一致性，并显著提升了下游语义分割性能，同时保持了几何和语义结构。
英文摘要：
Synthetic data provide low-cost, accurately annotated samples for geometry-sensitive vision tasks, but appearance and imaging differences between synthetic and real domains cause severe domain shift and degrade downstream performance. Unpaired synthetic-to-real translation can reduce this gap without paired supervision, yet existing methods often face a trade-off between photorealism and structural stability: unconstrained generation may introduce deformation or spurious textures, while overly rigid constraints limit adaptation to real-domain statistics. We propose FD-DB, a frequency-decoupled dual-branch model that separates appearance transfer into low-frequency interpretable editing and high-frequency residual compensation. The interpretable branch predicts physically meaningful editing parameters (white balance, exposure, contrast, saturation, blur, and grain) to build a stable low-frequency appearance base with strong content preservation. The free branch complements fine details through residual generation, and a gated fusion mechanism combines the two branches under explicit frequency constraints to limit low-frequency drift. We further adopt a two-stage training schedule that first stabilizes the editing branch and then releases the residual branch to improve optimization stability. Experiments on the YCB-V dataset show that FD-DB improves real-domain appearance consistency and significantly boosts downstream semantic segmentation performance while preserving geometric and semantic structures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
