Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes

ArXi:2605.13591v1 Announce Type: new 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.