SIMSplat: 面向驾驶场景生成的语言对齐4D高斯泼溅
SIMSplat: Language-Aligned 4D Gaussian Splatting for Driving Scenario Generation
AI总结：
提出SIMSplat，一种基于场景图的4D高斯泼溅框架，通过嵌入语言对齐特征实现自由文本查询、对象级编辑和多智能体仿真，在驾驶场景生成中显著提升定位精度和任务完成率。
摘要：
利用真实世界传感器数据进行驾驶场景操作已成为传统驾驶模拟器的一种有前景的替代方案。尽管在语言控制和神经场景表示方面取得了进展，现有方法将定位、编辑和仿真视为松散连接的阶段，依赖于启发式对象定位、人工引导和单智能体验证，从而限制了语义表达能力并阻碍了可扩展、反应式场景生成。我们提出了SIMSplat，一种基于场景图的4D高斯泼溅的驾驶场景编辑器，并增强了语言对齐特征。通过将外观、运动和位置语义直接嵌入高斯场景图节点，SIMSplat使得重建场景可以通过自由形式的自然语言进行查询，从而在一个统一的框架内弥合语言理解与对象级编辑及多智能体仿真之间的鸿沟。基于这种语言基础场景图，SIMSplat支持多种编辑操作，包括细粒度的行人操作，同时多智能体路径细化模块将所有智能体的变化传播出去，以确保反应式、物理上合理的仿真。该流程进一步与视觉语言模型集成，用于自动场景挖掘。实验表明，SIMSplat的基线定位精度提高了一倍以上，实现了最高的任务完成率，并在各种驾驶场景中产生了最低的失败率。
英文摘要：
Driving scene manipulation using real-world sensor data has emerged as a promising alternative to traditional driving simulators. Despite advances in language control and neural scene representations, existing methods treat grounding, editing, and simulation as loosely connected stages, relying on heuristic object localization, manual guidance, and single-agent validation, thereby constraining semantic expressiveness and hindering scalable, reactive scenario generation. We introduce SIMSplat, a driving scene editor built on scene-graph-based 4D Gaussian Splatting augmented with language-aligned features. By embedding appearance, motion, and location semantics directly into Gaussian scene-graph nodes, SIMSplat makes reconstructed scenes queryable through free-form natural language, bridging language understanding to object-level editing and multi-agent simulation within a single framework. Building on this language-grounded scene graph, SIMSplat supports diverse edits including fine-grained pedestrian manipulation, while a multi-agent path refinement module propagates changes across all agents to ensure reactive, physically plausible simulations. The pipeline further integrates with Vision-Language Models for automated scenario mining. Experiments show that SIMSplat more than doubles baseline grounding accuracy, achieves the highest task completion rate, and produces the lowest failure rates across diverse driving scenarios.
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。
