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. Guided by this framework, we construct an automated pipeline that processes approximately 5M images with over 45M objects to generate 3D spatial VQA pairs across diverse tasks and scenes for VLM supervised fine-tuning. We also develop an RGB-D VLM incorporating metric-scale point maps as auxiliary inputs to further enhance spatial understanding. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on multiple spatial understanding and reasoning benchmarks, surpassing specialized spatial models and large proprietary systems such as Gemini-2.5-pro and GPT-5. Moreover, our analysis reveals clear dependencies among hierarchical task levels, offering new insights into how multi-level task design facilitates the emergence of 3D spatial intelligence.

翻译：实现类人空间智能的视觉-语言模型（VLM）需要从二维观测中推断三维结构，识别三维空间中物体的属性与关系，并执行高层次的空间推理。本文提出一种原则性的分层框架，将VLM中三维空间理解的学习分解为从几何感知到抽象空间推理的四个渐进复杂度层级。在该框架指导下，我们构建了一条自动化流水线，处理约500万张图像（涵盖超过4500万个物体），生成面向不同任务与场景的三维空间视觉问答（VQA）对，用于VLM的监督微调。同时，我们开发了一种RGB-D VLM，引入公制尺度点云图作为辅助输入以进一步提升空间理解能力。大量实验表明，我们的方法在多项空间理解与推理基准测试中取得了最先进性能，超越了专用空间模型及Gemini-2.5-pro、GPT-5等大型专有系统。此外，分析揭示了分层任务层级间的明确依赖关系，为多层级任务设计如何促进三维空间智能涌现提供了新见解。