Metric-Bench: Exploring In-context Spatial Metric Reasoning in VLMs for Indoor Scenes

2026-09-22

AI Summary

为了解决视觉语言模型在空间度量推理中的局限性，研究引入了Metric-Bench基准和MetricReasoner方法，通过上下文信息和强化学习提高模型的2D到3D映射能力。

Abstract

Metric reasoning is a critical and challenging task for Vision Language Models (VLMs), playing a pivotal role in embodied AI tasks such as robotic manipulation and autonomous navigation. However, current spatial reasoning remains bottlenecked by rigid pixel-level supervision; such localized optimization often compromises general multimodal intelligence, triggering performance degradation or catastrophic forgetting of broad reasoning capabilities. To address these limitations, we introduce Metric-Bench, a focused benchmark designed to guide metric-spatial reasoning using contextual information. By incorporating in-image reference objects with known physical dimensions, Metric-Bench guides models to implicitly learn the 2D-to-3D mapping without camera intrinsics. We further present MetricReasoner, a task-adapted reinforcement fine-tuning recipe for reference-grounded metric reasoning, using structured prompts and verifiable numerical rewards. Extensive experiments on Metric-Bench demonstrate that our approach significantly enhances spatial metric understanding, outperforming existing and even larger proprietary models by 43.1\%, while improving downstream embodied performance over a spatial-specialized counterpart by 30.4\% on RoboSpatial overall accuracy and 9.3\% on ERQA, and additionally delivering consistent gains on general benchmarks (15.9\% on V$\star$Bench, 88.9\% on BLINK), indicating that the proposed adaptation does not necessarily compromise general VLM capabilities.

Problem

• Metric Reasoning
• Vision Language Models
• Spatial Reasoning
• Embodied AI

Innovation

• Metric-Bench
• In-context Spatial Metric Reasoning
• 2D-to-3D Mapping
• MetricReasoner
• Reinforcement Fine-tuning