MonoSR: Open-Vocabulary Spatial Reasoning on Monocular Images

Spatial reasoning from a single RGB image, including the inference of distances, sizes, and inter-object relationships without depth sensors or multiple views, remains a critical blind spot for current Vision Language Models (VLMs). Existing benchmarks either rely on multiview video sequences that expose explicit geometric cues, or are confined to indoor environments too small for model training. To close this gap, we introduce MonoSR, a large-scale dataset for open-world monocular spatial reasoning comprising over 1M QA pairs from 230K images spanning indoor, outdoor, and object-centric domains across 98 semantic categories. Every QA pair is guaranteed answerable from a single RGB image via a four-stage observability filter validated by human audit. Tasks are organized into three cognitive levels: foundational perception, perspective-aware imagination, and situational reasoning, supporting both large-scale training and fine-grained evaluation. Comprehensive benchmarking of state-of-the-art openand closed-source VLMs reveals consistent limitations across all three domains, with object-centric metric estimation emerging as the most challenging frontier. We further conduct a systematic auxiliary information study by injecting scene context, 2D visual prompts, and 3D bounding boxes, allowing us to quantify the geometric gap that future monocular perception modules must close and provide actionable design guidelines. Our project page is available at this link.