发表机构
Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出AstraLOD3，利用通用多模态模型Astra在智能体框架内零样本重建LOD3建筑模型，结合多视图图像和点云，达到平均FRDS 0.9647，证明其可替代专用流程。
AI 中文摘要
自动化的LOD3建筑建模通常依赖于专门构建的几何或基于学习的流程，这限制了其在异构建筑和不同输入证据条件下的灵活性。本研究探讨了通用多模态基础模型Astra能否通过在有限自主性的智能体框架内进行LOD3建筑模型的零样本重建来解决这些局限性。AstraLOD3将多视角图像、校准相机和过滤后的稀疏SfM点云与自然语言重建规范相结合，同时Astra智能体使用Python和Blender动态选择并执行计算程序。在35次运行中（包括24个基准建筑），AstraLOD3实现了平均FRDS为0.9647，几何一致性与此前专门构建的方法相当。受控消融实验进一步揭示了重建指导、证据模态、模型配置和运行间变异性的影响。结果表明，结构化的LOD3重建可以被表述为受约束的智能体过程，而非固定流程。未来工作将研究自适应细化、用户引导修正、任务特定专业化以及损伤感知重建。
英文摘要
Automated LOD3 building modeling typically relies on purpose-built geometric or learning-based pipelines, limiting flexibility across heterogeneous buildings and input evidence conditions. This study investigates whether Astra, a general-purpose multimodal foundation model, can address these limitations through zero-shot reconstruction of LOD3 building models within an agentic framework under bounded autonomy. AstraLOD3 combines multi-view images, calibrated cameras, and a filtered sparse SfM point cloud with a natural-language reconstruction specification, while the Astra agent dynamically selects and executes computational procedures using Python and Blender. Across 35 runs, including 24 benchmark buildings, AstraLOD3 achieved a mean FRDS of 0.9647 and geometric agreement comparable to that of previous purpose-built methods. Controlled ablations further revealed the effects of reconstruction guidance, evidence modalities, model configuration, and run-to-run variability. The results demonstrate that structured LOD3 reconstruction can be formulated as a constrained agentic process rather than as a fixed pipeline. Future work will investigate adaptive refinement, user-guided correction, task-specific specialization, and damage-aware reconstruction.
