用航拍图生成逼真步行视角城市画面，不碰地面数据也能建3D城。
AerialGo: Walking-through City View Generation from Aerial Perspectives
- 用多视角扩散模型，从航拍图合成地面视角
- 生成画面真实度高，结构连贯性强，无需采集地面数据
- 适合城市规划、导航、元宇宙等需大规模建模的场景
高质量三维城市重建对城市规划、导航和增强现实/虚拟现实应用至关重要。然而，在全城范围内采集详细地面数据既费时费力，又引发关于车牌、人脸等敏感信息的隐私担忧。为此，我们提出AerialGo，一种新型框架，利用多视角扩散模型，仅基于可获取的航拍图像生成逼真的步行穿越城市视图，实现无需直接采集地面数据的可扩展、高保真城市重建。通过将地面视角生成条件化于航拍数据，AerialGo规避了地面影像固有的隐私风险。为支持模型训练，我们构建了AerialGo数据集，包含大规模航拍与地面视角图像对，附带相机参数和深度信息，专为生成式城市重建设计。实验表明，AerialGo显著提升了地面视角的真实感与结构一致性，提供了一种兼顾隐私保护与可扩展性的城市级三维建模方案。
原文摘要 · Abstract (English)
High-quality 3D urban reconstruction is essential for applications in urban planning, navigation, and AR/VR. However, capturing detailed ground-level data across cities is both labor-intensive and raises significant privacy concerns related to sensitive information, such as vehicle plates, faces, and other personal identifiers. To address these challenges, we propose AerialGo, a novel framework that generates realistic walking-through city views from aerial images, leveraging multi-view diffusion models to achieve scalable, photorealistic urban reconstructions without direct ground-level data collection. By conditioning ground-view synthesis on accessible aerial data, AerialGo bypasses the privacy risks inherent in ground-level imagery. To support the model training, we introduce AerialGo dataset, a large-scale dataset containing diverse aerial and ground-view images, paired with camera and depth information, designed to support generative urban reconstruction. Experiments show that AerialGo significantly enhances ground-level realism and structural coherence, providing a privacy-conscious, scalable solution for city-scale 3D modeling.
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