AI RESEARCH
R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation
arXiv CS.LG
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ArXi:2506.07826v2 Announce Type: replace-cross Validating autonomous driving (AD) systems requires diverse and safety-critical testing, making photorealistic virtual environments essential. Traditional simulation platforms, while controllable, are resource-intensive to scale and often suffer from a domain gap with real-world data. In contrast, neural reconstruction methods like 3D Gaussian Splatting (3DGS) offer a scalable solution for creating photorealistic digital twins of real-world driving scenes.