High-fidelity and controllable 3D simulation is essential for addressing the long-tail data scarcity in Autonomous Driving (AD), yet existing methods struggle to simultaneously achieve photorealistic rendering and interactive traffic editing. Current approaches often falter in large-angle novel view synthesis and suffer from geometric or lighting artifacts during asset manipulation. To address these challenges, we propose SymDrive, a unified diffusion-based framework capable of joint high-quality rendering and scene editing. We introduce a Symmetric Auto-regressive Online Restoration paradigm, which constructs paired symmetric views to recover fine-grained details via a ground-truth-guided dual-view formulation and utilizes an auto-regressive strategy for consistent lateral view generation. Furthermore, we leverage this restoration capability to enable a training-free harmonization mechanism, treating vehicle insertion as context-aware inpainting to ensure seamless lighting and shadow consistency. Extensive experiments demonstrate that SymDrive achieves state-of-the-art performance in both novel-view enhancement and realistic 3D vehicle insertion.

翻译：高保真且可控的三维模拟对于解决自动驾驶（AD）中的长尾数据稀缺问题至关重要，然而现有方法难以同时实现照片级真实感渲染与交互式交通编辑。当前方法在大角度新视角合成中常常表现不佳，且在资产操作过程中易出现几何或光照伪影。为应对这些挑战，我们提出了SymDrive，一个统一的基于扩散的框架，能够联合进行高质量渲染与场景编辑。我们引入了一种对称自回归在线修复范式，该范式通过构建成对的对称视图，利用真实标注引导的双视图公式来恢复细粒度细节，并采用自回归策略以生成一致性的侧向视图。此外，我们利用这种修复能力实现了一种免训练的协调机制，将车辆插入视为上下文感知的图像修复，以确保光照与阴影的无缝一致性。大量实验表明，SymDrive在新视角增强与逼真的三维车辆插入两方面均达到了最先进的性能。