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.

翻译：空间理解是人类级智能的基本基石。然而，当前研究主要集中于特定领域的数据生产，导致一个关键空白：缺少能够充分释放高质量空间数据潜力的规范化开源引擎。为填补这一空白，我们阐明了稳健数据生成系统的设计原则，并推出OpenSpatial——一个面向高质量、大规模可扩展性、广泛任务多样性和优化效率的开源数据引擎。OpenSpatial采用3D边界框作为基本基元，构建了涵盖五项基础任务的全面数据层级：空间测量（SM）、空间关系（SR）、相机感知（CP）、多视角一致性（MC）以及场景感知推理（SAR）。借助这一可扩展的基础架构，我们整理了OpenSpatial-3M——一个包含300万个高保真样本的大规模数据集。广泛评估表明，基于该数据集训练的多功能模型在多种空间推理基准上取得了最先进的性能。值得注意的是，性能最优模型平均相对提升达19%。此外，我们系统分析了数据属性如何影响空间感知。通过开源引擎与300万规模数据集，我们为加速未来空间智能研究提供了坚实基础。