Real2Sim：一种用于自动驾驶场景的物理驱动且可编辑的高斯点扩散框架
Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes
AI总结：
Real2Sim结合4D高斯点扩散与可微材料点法求解器，实现动态驾驶场景的连续高斯原语重建，支持实例级编辑，并模拟真实物体间交互，提升自动驾驶场景生成的物理真实性和编辑灵活性。
摘要：
可靠的自动驾驶依赖于大规模、高质量的数据和稳健的模型。然而，手动数据收集成本高，传统模拟存在持续的现实差距。尽管近期生成框架和辐射场方法提升了视觉保真度，但它们在时间和空间一致性上仍有不足，且无法确保物理意识行为，限制了其在驾驶场景生成中的应用。为了解决这些挑战，我们提出了Real2Sim，一种结合4D高斯点扩散（4DGS）与可微材料点法（MPM）求解器的统一框架。Real2Sim明确重建动态驾驶场景为时间连续的高斯原语，支持实例级编辑，并模拟真实物体间和物体与环境的交互。该框架使能够生成具有物理意识的高保真多样化可编辑场景，包括碰撞和碰撞后轨迹等具有挑战性的边缘案例。在Waymo开放数据集上的实验验证了Real2Sim在渲染、重建、编辑和物理模拟方面的能力，展示了其作为下游任务（如感知、跟踪、轨迹预测和端到端策略学习）数据生成可扩展工具的潜力。
英文摘要：
Reliable autonomous driving relies on large-scale, well-labeled data and robust models. However, manual data collection is resource-intensive, and traditional simulation suffers from a persistent reality gap. While recent generative frameworks and radiance-field methods improve visual fidelity, they still struggle with temporal and spatial consistency and cannot ensure physics-aware behavior, limiting their applicability to driving scenario generation. To address these challenges, we propose Real2Sim, an unified framework that combines 4D Gaussian Splatting (4DGS) with a differentiable Material Point Method (MPM) solver. Real2Sim explicitly reconstructs dynamic driving scenes as temporally continuous Gaussian primitives, supports instance-level editing, and simulates realistic object-object and object-environment interactions. This framework enables physics-aware, high-fidelity synthesis of diverse, editable scenarios, including challenging corner cases such as collisions and post-impact trajectories. Experiments on the Waymo Open Dataset validate Real2Sim's capabilities in rendering, reconstruction, editing, and physics simulation, demonstrating its potential as a scalable tool for data generation in downstream tasks such as perception, tracking, trajectory prediction, and end-to-end policy learning.
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