AI RESEARCH
HiSpatial: Taming Hierarchical 3D Spatial Understanding in Vision-Language Models
arXiv CS.CV
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ArXi:2603.25411v1 Announce Type: new Achieving human-like spatial intelligence for vision-language models (VLMs) requires inferring 3D structures from 2D observations, recognizing object properties and relations in 3D space, and performing high-level spatial reasoning. In this paper, we propose a principled hierarchical framework that decomposes the learning of 3D spatial understanding in VLMs into four progressively complex levels, from geometric perception to abstract spatial reasoning.