AI RESEARCH
Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images
arXiv CS.CV
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ArXi:2604.19257v1 Announce Type: new Creating realistic and simulation-ready 3D assets is crucial for autonomous driving research and virtual environment construction. However, existing 3D vehicle generation methods are often trained on synthetic data with significant domain gaps from real-world distributions. The generated models often exhibit arbitrary poses and undefined scales, resulting in poor visual consistency when integrated into driving scenes.