LiteReality-Agent：用于可交互三维室内场景重建的智能体系统
LiteReality-Agent: An Agentic System for Interactable 3D Indoor Scene Reconstruction
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中文总结 AI 辅助
LiteReality-Agent将三维重建视为编码问题，通过观察-编辑-验证框架迭代编辑Python脚本，生成几何精确、视觉真实且仿真兼容的室内场景，优于Astra和Fable等前沿模型。
中文摘要 AI 辅助
我们提出了LiteReality-Agent，一个从RGB-D扫描中将真实室内环境重建为逼真、可铰接且可仿真就绪的三维场景的智能体系统。其核心在于，LiteReality-Agent将三维重建表述为一个编码问题，其中编码智能体使用专门工具收集证据，并迭代地编辑一个Python脚本（this http URL），该脚本可被执行以生成房间的三维数字孪生。基于这一表述，我们开发了一个稳健的观察-编辑-验证框架，该框架在整个重建过程中支持证据收集、测量、验证、布局优化、仿真就绪性和质量控制。LiteReality-Agent生成的高质量重建结果适用于仿真和下游具身人工智能任务。此外，随着智能体能力持续快速提升，LiteReality-Agent所引入的系统仍然是未来智能体的强大编排框架：它为其配备了专门工具、结构化工作流程和稳健的验证机制，这些机制显著提高了重建质量和可靠性。我们证明了LiteReality-Agent生成的重建结果在几何精度、视觉真实感和仿真兼容性方面均优于近期前沿模型（如Astra和Fable）所生成的结果。因此，我们将LiteReality-Agent视为构建稳健的真实到仿真系统的一个实用且重要的基础组件。源代码和数据采集应用程序均已公开可用。代码：this https URL
英文摘要
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/
发表机构
- University of Cambridge（剑桥大学）
- Imperial College London（伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
