CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912836586831872 |
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| author | Xu, Haotian Hu, Yue Zhu, Zhengqiu Gao, Chen Wang, Ziyou Rao, Junreng Lu, Wenhao Li, Weishi Yin, Quanjun Li, Yong |
| author_facet | Xu, Haotian Hu, Yue Zhu, Zhengqiu Gao, Chen Wang, Ziyou Rao, Junreng Lu, Wenhao Li, Weishi Yin, Quanjun Li, Yong |
| contents | Cross-view spatial reasoning is essential for embodied AI, underpinning spatial understanding, mental simulation and planning in complex environments. Existing benchmarks primarily emphasize indoor or street settings, overlooking the unique challenges of open-ended urban spaces characterized by rich semantics, complex geometries, and view variations. To address this, we introduce CityCube, a systematic benchmark designed to probe cross-view reasoning capabilities of current VLMs in urban settings. CityCube integrates four viewpoint dynamics to mimic camera movements and spans a wide spectrum of perspectives from multiple platforms, e.g., vehicles, drones and satellites. For a comprehensive assessment, it features 5,022 meticulously annotated multi-view QA pairs categorized into five cognitive dimensions and three spatial relation expressions. A comprehensive evaluation of 33 VLMs reveals a significant performance disparity with humans: even large-scale models struggle to exceed 54.1% accuracy, remaining 34.2% below human performance. By contrast, small-scale fine-tuned VLMs achieve over 60.0% accuracy, highlighting the necessity of our benchmark. Further analyses indicate the task correlations and fundamental cognitive disparity between VLMs and human-like reasoning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_14339 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments Xu, Haotian Hu, Yue Zhu, Zhengqiu Gao, Chen Wang, Ziyou Rao, Junreng Lu, Wenhao Li, Weishi Yin, Quanjun Li, Yong Computer Vision and Pattern Recognition Artificial Intelligence Cross-view spatial reasoning is essential for embodied AI, underpinning spatial understanding, mental simulation and planning in complex environments. Existing benchmarks primarily emphasize indoor or street settings, overlooking the unique challenges of open-ended urban spaces characterized by rich semantics, complex geometries, and view variations. To address this, we introduce CityCube, a systematic benchmark designed to probe cross-view reasoning capabilities of current VLMs in urban settings. CityCube integrates four viewpoint dynamics to mimic camera movements and spans a wide spectrum of perspectives from multiple platforms, e.g., vehicles, drones and satellites. For a comprehensive assessment, it features 5,022 meticulously annotated multi-view QA pairs categorized into five cognitive dimensions and three spatial relation expressions. A comprehensive evaluation of 33 VLMs reveals a significant performance disparity with humans: even large-scale models struggle to exceed 54.1% accuracy, remaining 34.2% below human performance. By contrast, small-scale fine-tuned VLMs achieve over 60.0% accuracy, highlighting the necessity of our benchmark. Further analyses indicate the task correlations and fundamental cognitive disparity between VLMs and human-like reasoning. |
| title | CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.14339 |