Not a Photo, but a Space You Can Walk Into: Reconstructing 50 Korean Homes with 3DGS
Blog post from Superb AI
Hyun Kim describes how Phase 2 of Korea’s Sovereign AI Foundation Model Project reconstructed 50 Korean homes into simulator-ready digital environments for robot learning, rather than producing conventional panoramic or viewing-focused scans. Using 3D Gaussian Splatting, the team created freely navigable visual backgrounds from ordinary camera footage while prioritizing accurate real-world scale, ground-plane calibration, and structural reliability over visual fidelity. Since 3DGS does not separate scene objects, interactive items such as doors, drawers, dishes, and clothing are added as distinct physical meshes that support collision and manipulation, while wall and floor planes help reduce physically implausible interactions. The work exposed reconstruction difficulties in narrow rooms, and a related office digital-twin project led to improvements in multi-person and robot avoidance behavior in NVIDIA Isaac Sim. The effort is presented within a growing global competition to build physical AI training infrastructure, where reusable, simulation-compatible digital assets may determine how effectively real-world facilities can support robot training.
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