OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence

Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses ondomain-specific data production, leaving a critical void: the absence of aprincipled, open-source engine capable of fully unleashing the potentialof high-quality spatial data. To bridge this gap, we elucidate the designprinciples of a robust data generation system and introduce OpenSpa-tial—an open-source data engine engineered for high quality, extensivescalability, broad task diversity, and optimized efficiency. OpenSpatialadopts 3D bounding boxes as the fundamental primitive to constructa comprehensive data hierarchy across five foundational tasks: SpatialMeasurement (SM), Spatial Relationship (SR), Camera Perception (CP),Multi-view Consistency (MC), and Scene-Aware Reasoning (SAR). Lever-aging this scalable infrastructure, we curate OpenSpatial-3M, a large-scale dataset comprising 3 million high-fidelity samples. Extensive eval-uations demonstrate that versatile models trained on our dataset achievestate-of-the-art performance across a wide spectrum of spatial reasoningbenchmarks. Notably, the best-performing model exhibits a substantialaverage improvement of 19%, relatively. Furthermore, we provide a sys-tematic analysis of how data attributes influence spatial perception. Byopen-sourcing both the engine and the 3M-scale dataset, we provide arobust foundation to accelerate future research in spatial intelligence.Code: github.com/VINHYU/OpenSpatialDataset: huggingface.co/datasets/JoyAI-Image-OpenSpatial