本文提出了一种端到端、代理驱动的LoD3建筑立面开口重建流程，可直接生成符合CityGML标准的三维城市模型输出。与依赖监督式语义分割、因而需大量人工标注训练数据的现有方法不同，所提方法采用零样本分割策略，在eTRIMS数据集基准测试中显著降低标注成本的同时仍保持优异性能。另一关键贡献在于强制保障正确的组成部分层级关系（partonomic hierarchies），从而生成符合CityGML标准的LoD3建筑模型。除重建流程本身外，本工作还引入一种面向立面重建的新评估指标——立面特征距离（Facade Feature Distance, FFD）。不同于mIoU或FRDS等传统指标（主要通过像素级重叠度衡量相似性），FFD在视觉Transformer提取的高层特征空间中计算距离，兼顾语义正确性与建筑布局结构，从而更适切地评估立面重建质量。所提出的重建流程与评估策略共同为语义增强型三维城市模型的自动化生成与分析提供了实用且可扩展的解决方案。相关代码已开源：https://github.com/hcu-cml/citydb-SVI2LoD3-ai。

This paper presents an end-to-end, agent-driven pipeline for the LoD3 reconstruction of facade openings in 3D city models, producing directly usable CityGML-conform outputs. In contrast to existing approaches that rely on supervised semantic segmentation and therefore require large amounts of manually annotated training data, the proposed method employs a zero-shot segmentation strategy. This substantially reduces the annotation effort while still achieving strong performance in our benchmark on the eTRIMS dataset. A further key contribution is the enforcement of correct partonomic hierarchies, thereby producing CityGML-conform LoD3 building models. Beyond the reconstruction pipeline itself, this work also introduces a novel evaluation metric for facade reconstruction, termed Facade Feature Distance (FFD). Unlike conventional metrics such as mIoU or FRDS, which assess similarity primarily through pixel-wise overlap, FFD measures distance in a high-level feature space derived from a vision transformer. In doing so, it captures both semantic correctness and architectural layout, providing a more suitable assessment of facade reconstruction quality. The proposed pipeline and evaluation strategy together offer a practical and scalable contribution toward the automated generation and analysis of semantically enriched 3D city models. The developed code is published at: https://github.com/hcu-cml/citydb-SVI2LoD3-ai.