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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2503.16776 |
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| _version_ | 1866909547177705472 |
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| author | Bieri, Valentin Zamboni, Marco Blumer, Nicolas S. Chen, Qingxuan Engelmann, Francis |
| author_facet | Bieri, Valentin Zamboni, Marco Blumer, Nicolas S. Chen, Qingxuan Engelmann, Francis |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenCity3D: What do Vision-Language Models know about Urban Environments? Bieri, Valentin Zamboni, Marco Blumer, Nicolas S. Chen, Qingxuan Engelmann, Francis Computer Vision and Pattern Recognition 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 |
| title | OpenCity3D: What do Vision-Language Models know about Urban Environments? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.16776 |