While multi-modality large language models excel in object-centric or indoor scenarios, scaling them to 3D city-scale environments remains a formidable challenge. To bridge this gap, we propose 3DCity-LLM, a unified framework designed for 3D city-scale vision-language perception and understanding. 3DCity-LLM employs a coarse-to-fine feature encoding strategy comprising three parallel branches for target object, inter-object relationship, and global scene. To facilitate large-scale training, we introduce 3DCity-LLM-1.2M dataset that comprises approximately 1.2 million high-quality samples across seven representative task categories, ranging from fine-grained object analysis to multi-faceted scene planning. This strictly quality-controlled dataset integrates explicit 3D numerical information and diverse user-oriented simulations, enriching the question-answering diversity and realism of urban scenarios. Furthermore, we apply a multi-dimensional protocol based on text-similarity metrics and LLM-based semantic assessment to ensure faithful and comprehensive evaluations for all methods. Extensive experiments on two benchmarks demonstrate that 3DCity-LLM significantly outperforms existing state-of-the-art methods, offering a promising and meaningful direction for advancing spatial reasoning and urban intelligence. The source code and dataset are available at https://github.com/SYSU-3DSTAILab/3D-City-LLM.

翻译：尽管多模态大语言模型在以物体为中心或室内场景中表现出色，但将其扩展到三维城市场景中仍是一项严峻挑战。为弥补这一差距，我们提出3DCity-LLM，一个专为三维城市级视觉语言感知与理解设计的统一框架。该框架采用由粗到精的特征编码策略，包含三个并行分支，分别用于目标对象、对象间关系以及全局场景建模。为支持大规模训练，我们构建了3DCity-LLM-1.2M数据集，包含约120万个高质量样本，涵盖从细粒度目标分析到多方面场景规划的七类代表性任务。该数据集经过严格质量控制，整合了显式的三维数值信息和多种面向用户的模拟场景，增强了问答多样性与城市场景的真实性。此外，我们基于文本相似度指标和大语言模型语义评估，设计了多维评估协议，以确保对所有方法进行忠实且全面的评测。在两个基准上的大量实验表明，3DCity-LLM显著优于现有最先进方法，为推进空间推理与城市智能提供了有前景且有价值的研究方向。源代码及数据集已开源至 https://github.com/SYSU-3DSTAILab/3D-City-LLM。