OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914460988342272 |
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| author | Liu, Jianhui Sun, Haoze Li, Wenbo Zhang, Yanbing Yang, Rui Zhu, Zhiliang Yang, Yijun Zheng, Shenghe Jiang, Nan Jiang, Jiaxiu Huang, Haoyang Wong, Tien-Tsin Duan, Nan Qi, Xiaojuan |
| author_facet | Liu, Jianhui Sun, Haoze Li, Wenbo Zhang, Yanbing Yang, Rui Zhu, Zhiliang Yang, Yijun Zheng, Shenghe Jiang, Nan Jiang, Jiaxiu Huang, Haoyang Wong, Tien-Tsin Duan, Nan Qi, Xiaojuan |
| contents | Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specific data production, leaving a critical void: the absence of a principled, open-source engine capable of fully unleashing the potential of high-quality spatial data. To bridge this gap, we elucidate the design principles of a robust data generation system and introduce OpenSpatial -- an open-source data engine engineered for high quality, extensive scalability, broad task diversity, and optimized efficiency. OpenSpatial adopts 3D bounding boxes as the fundamental primitive to construct a comprehensive data hierarchy across five foundational tasks: Spatial Measurement (SM), Spatial Relationship (SR), Camera Perception (CP), Multi-view Consistency (MC), and Scene-Aware Reasoning (SAR). Leveraging this scalable infrastructure, we curate OpenSpatial-3M, a large-scale dataset comprising 3 million high-fidelity samples. Extensive evaluations demonstrate that versatile models trained on our dataset achieve state-of-the-art performance across a wide spectrum of spatial reasoning benchmarks. Notably, the best-performing model exhibits a substantial average improvement of 19 percent, relatively. Furthermore, we provide a systematic analysis of how data attributes influence spatial perception. By open-sourcing both the engine and the 3M-scale dataset, we provide a robust foundation to accelerate future research in spatial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07296 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence Liu, Jianhui Sun, Haoze Li, Wenbo Zhang, Yanbing Yang, Rui Zhu, Zhiliang Yang, Yijun Zheng, Shenghe Jiang, Nan Jiang, Jiaxiu Huang, Haoyang Wong, Tien-Tsin Duan, Nan Qi, Xiaojuan Computation and Language Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specific data production, leaving a critical void: the absence of a principled, open-source engine capable of fully unleashing the potential of high-quality spatial data. To bridge this gap, we elucidate the design principles of a robust data generation system and introduce OpenSpatial -- an open-source data engine engineered for high quality, extensive scalability, broad task diversity, and optimized efficiency. OpenSpatial adopts 3D bounding boxes as the fundamental primitive to construct a comprehensive data hierarchy across five foundational tasks: Spatial Measurement (SM), Spatial Relationship (SR), Camera Perception (CP), Multi-view Consistency (MC), and Scene-Aware Reasoning (SAR). Leveraging this scalable infrastructure, we curate OpenSpatial-3M, a large-scale dataset comprising 3 million high-fidelity samples. Extensive evaluations demonstrate that versatile models trained on our dataset achieve state-of-the-art performance across a wide spectrum of spatial reasoning benchmarks. Notably, the best-performing model exhibits a substantial average improvement of 19 percent, relatively. Furthermore, we provide a systematic analysis of how data attributes influence spatial perception. By open-sourcing both the engine and the 3M-scale dataset, we provide a robust foundation to accelerate future research in spatial intelligence. |
| title | OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.07296 |