OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence
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.
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Jianhui Liu
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Haoze Sun
claimed_verified
2026-04-10T13:34:15.340Z
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Haoze Sun
VINHYU
user
VINHYU
69d76ca3a3ce5cf29b5e7429
Wenbo Li
69d76ca3a3ce5cf29b5e742a
Yanbing Zhang
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Rui Yang
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Zhiliang Zhu
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Yijun Yang
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Shenghe Zheng
69d76ca3a3ce5cf29b5e742f
Nan Jiang
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Jiaxiu Jiang
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Haoyang Huang
69d76ca3a3ce5cf29b5e7432
Tien-Tsin Wong
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Nan Duan
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Xiaojuan Qi
OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence
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.
OpenSpatial presents an open-source data engine for spatial reasoning tasks using 3D bounding boxes, creating a large-scale dataset and achieving state-of-the-art performance in spatial perception benchmarks.
spatial reasoning 3D bounding boxes data hierarchy spatial measurement spatial relationship camera perception multi-view consistency scene-aware reasoning large-scale dataset state-of-the-art performance