Sat2RealCity：基于卫星图像的几何感知与外观可控的3D城市生成
Sat2RealCity: Geometry-Aware and Appearance-Controllable 3D Urban Generation from Satellite Imagery
AI总结：
针对卫星图像3D城市生成问题，Sat2RealCity框架利用对象级3D生成先验，通过分解城市为地理定位建筑实体，结合构建的数据集，引入空间定位策略、设计外观引导机制及构建语义管道，实现良好的地理对齐、风格一致和资产合成。
摘要：
从卫星图像生成3D城市是可扩展数字孪生和真实世界模拟环境的重要任务。现有方法主要依赖场景级生成范式，存在诸多局限。为此提出Sat2RealCity框架，利用对象级3D生成先验从卫星图像进行可扩展城市合成。该框架将城市分解为地理定位的建筑实体，借助构建的BuildVerse3D数据集，引入OSM引导的空间定位策略，设计外观引导的可控生成机制，构建MLLM驱动的语义管道。实验表明，Sat2RealCity在地理对齐、区域风格一致性和城市资产合成方面表现出色。
英文摘要：
3D urban generation from satellite imagery is an important task for scalable digital twins and real-world simulation environments. Existing approaches primarily rely on scene-level generation paradigms, which often require large-scale 3D city assets and struggle with controllability, geographic alignment, and realistic appearance grounding in real-world urban environments. To address these limitations, we present Sat2RealCity, a grounded urban generation framework that leverages object-level 3D generative priors for scalable city synthesis from satellite imagery. Our framework decomposes cities into geographically grounded building entities, enabling the reuse of pretrained object-level 3D generative priors while preserving real-world spatial structures. Supported by our constructed BuildVerse3D dataset, (1) we introduce an OpenStreetMap (OSM)-guided spatial grounding strategy to inject geospatial constraints into the 3D generation process; (2) we design an appearance-guided controllable generation mechanism for realistic architectural appearance and regional style consistency; and (3) we construct an MLLM-powered semantic pipeline for regional appearance understanding and semantic-aware appearance synthesis. Extensive experiments demonstrate that Sat2RealCity achieves strong geographic alignment, regional stylistic consistency, and plausible urban asset synthesis compared with existing urban generation and 3D asset generation approaches.
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