Abstract
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.
Abstract (translated)
高保真且可控的三维仿真对于解决自主驾驶（AD）中的长尾数据稀缺问题至关重要，但现有方法难以同时实现逼真的渲染和互动式交通编辑。目前的方法在处理大角度新颖视角合成时常常表现出色不足，并在资产操作过程中出现几何或光照瑕疵。为了解决这些挑战，我们提出了SymDrive，这是一种统一的基于扩散框架的方法，能够联合高质量的渲染与场景编辑。我们引入了对称自回归在线恢复范式，该范式通过构造配对的对称视图并采用一个以真实标签引导的双视角方案来恢复细微细节，并利用一种自回归策略生成一致侧向视图。此外，我们借助这种恢复能力实现了一种无训练调和机制，在处理车辆插入时将其视为上下文感知的修复过程，确保光照和阴影的一致性。广泛的实验表明，SymDrive在新颖视角增强及逼真的三维车辆插入方面均达到了当前的最佳性能水平。
URL
https://arxiv.org/abs/2512.21618
