用对称自回归恢复实现逼真可控的自动驾驶仿真
SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration
- 通过成对对称视图与自回归生成，提升新视角还原质量
- 在真实车辆插入任务中实现光照阴影无缝融合，无须额外训练
- 适合需要高保真场景编辑的自动驾驶仿真研究者
高保真且可交互的3D仿真对解决自动驾驶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渲染与动态交通编辑。当前技术在大角度新视角合成上表现不佳，且在资产操作时易产生几何或光照伪影。为此，我们提出SymDrive，一种基于扩散模型的统一框架，可联合实现高质量渲染与场景编辑。引入对称自回归在线恢复范式，通过真实图像引导的双视图构型恢复细粒度细节，并采用自回归策略生成一致的侧向视角。此外，利用该恢复能力实现无需训练的调和机制，将车辆插入视为上下文感知的图像修复，确保光照与阴影一致性。大量实验表明，SymDrive在新视角增强与真实3D车辆插入任务上均达到当前最优性能。
原文摘要 · Abstract (English)
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.
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