SpatialForge: 从开放世界2D图像中提升3D感知空间推理
SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images
AI总结：
本文提出SpatialForge，通过大规模合成数据提升空间推理能力，构建了包含1000万对空间问答的大型数据集，验证了2D数据扩展对3D空间推理的有效性。
摘要：
近期大型视觉-语言模型（VLMs）在语义理解上表现出色，但普遍在空间推理方面存在困难，如深度排序和精确坐标定位等基本几何任务。最近的研究引入了基于场景中心数据集的空间监督（如多视角扫描或室内视频），但受限于底层场景数量有限。为此，我们提出SpatialForge，一种可扩展的数据合成管道，将野外2D图像转化为空间推理监督。我们的方法将空间推理分解为感知与关系，构建覆盖深度、布局及视角依赖推理的结构化监督信号，并通过自动验证确保数据质量。基于此管道，我们构建了包含1000万对空间问答的SpatialForge-10M大型数据集。在多个空间推理基准上的广泛实验表明，训练于SpatialForge-10M显著提升了标准VLMs的空间推理能力，凸显了2D数据扩展对3D感知空间推理的有效性。
英文摘要：
Recent advancements in Large Vision-Language Models (VLMs) have demonstrated exceptional semantic understanding, yet these models consistently struggle with spatial reasoning, often failing at fundamental geometric tasks such as depth ordering and precise coordinate grounding. Recent efforts introduce spatial supervision from scene-centric datasets (e.g., multi-view scans or indoor video), but are constrained by the limited number of underlying scenes. As a result, the scale and diversity of such data remain significantly smaller than those of web-scale 2D image collections. To address this limitation, we propose SpatialForge, a scalable data synthesis pipeline that transforms in-the-wild 2D images into spatial reasoning supervision. Our approach decomposes spatial reasoning into perception and relation, and constructs structured supervision signals covering depth, layout, and viewpoint-dependent reasoning, with automatic verification to ensure data quality. Based on this pipeline, we build SpatialForge-10M, a large-scale dataset containing 10 million spatial QA pairs. Extensive experiments across multiple spatial reasoning benchmarks demonstrate that training on SpatialForge-10M significantly improves the spatial reasoning ability of standard VLMs, highlighting the effectiveness of scaling 2D data for 3D-aware spatial reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
