用视觉语言模型分析城市环境，实现零样本城市级智能评估
OpenCity3D: What do Vision-Language Models know about Urban Environments?
- 基于航拍图像重建3D城市，用VLM完成高阶任务
- 零样本/少样本下准确预测人口密度、房价、犯罪率等
- 适合城市规划、政策制定与环境监测人员使用
视觉语言模型（VLMs）在3D场景理解中展现出巨大潜力，但主要应用于室内空间或自动驾驶，聚焦于分割等底层任务。本文将VLM扩展至城市尺度环境，利用多视角航拍图像生成的3D重建数据，提出OpenCity3D方法，解决人口密度估计、建筑年龄分类、房产价格预测、犯罪率评估和噪声污染评价等高层任务。实验表明，OpenCity3D具备出色的零样本与少样本能力，可适应新场景。本研究建立了语言驱动的城市分析新范式，适用于城市规划、政策制定与环境监测等领域。项目页面：opencity3d.github.io
原文摘要 · Abstract (English)
Vision-language models (VLMs) show great promise for 3D scene understanding but are mainly applied to indoor spaces or autonomous driving, focusing on low-level tasks like segmentation. This work expands their use to urban-scale environments by leveraging 3D reconstructions from multi-view aerial imagery. We propose OpenCity3D, an approach that addresses high-level tasks, such as population density estimation, building age classification, property price prediction, crime rate assessment, and noise pollution evaluation. Our findings highlight OpenCity3D's impressive zero-shot and few-shot capabilities, showcasing adaptability to new contexts. This research establishes a new paradigm for language-driven urban analytics, enabling applications in planning, policy, and environmental monitoring. See our project page: opencity3d.github.io
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