[
    {
        "id": "osp-24031",
        "type": "article-journal",
        "title": "LiteReality-Agent: An Agentic System for Interactable 3D Indoor Scene Reconstruction",
        "author": [
            {
                "family": "Huang",
                "given": "Zhening"
            },
            {
                "family": "Li",
                "given": "Yueyan"
            },
            {
                "family": "Chiu",
                "given": "Johnathan"
            },
            {
                "family": "Lyu",
                "given": "Xiaoyang"
            },
            {
                "family": "Zhou",
                "given": "Matt"
            },
            {
                "family": "Yao",
                "given": "Yuxin"
            },
            {
                "family": "Lasenby",
                "given": "Joan"
            },
            {
                "family": "Wu",
                "given": "Shangzhe"
            }
        ],
        "URL": "https://omanscience.com/en/articles/litereality-agent-an-agentic-system-for-interactable-3d-indoor-scene-reconstruction",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "We present LiteReality-Agent, an agentic system for reconstructing real indoor environments as realistic, articulated, and simulation-ready 3D scenes from RGB-D scans. At its core, LiteReality-Agent formulates 3D reconstruction as a coding problem, in which a coding agent gathers evidence using specialised tools and iteratively edits a Python script, Room.py, which can be executed to produce a 3D digital twin of the room. With this formulation, we develop a robust observe-edit-verify harness that supports evidence gathering, measurement, verification, layout optimisation, simulation readiness, and quality control throughout the reconstruction process. LiteReality-Agent produces high-quality reconstructions suitable for simulation and downstream embodied AI tasks. Furthermore, as agent capabilities continue to improve rapidly, the system introduced by LiteReality-Agent remains a strong orchestration framework for future agents: it equips them with specialised tools, structured workflows, and robust verification mechanisms that substantially improve reconstruction quality and reliability. We demonstrate that LiteReality-Agent produces reconstructions that are more geometrically accurate, visually realistic, and simulation-compatible than those generated by recent frontier models, such as Astra and Fable. We therefore view LiteReality-Agent as a practical and important building block for robust real-to-sim systems. Both the source code and the data-capture application are publicly available. Code:https://github.com/LiteReality/LiteReality-Agent/"
    }
]