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

Yuling Xi; Haokai Zhang; Muzhi Zhu; Hao Zhong; Zongze Du; Hengyu Zhao; Chenchen Jing; Yufei Yin

cs.CVSep 22, 2026

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

Authors: Yuling Xi, Haokai Zhang, Muzhi Zhu, Hao Zhong, Zongze Du, Hengyu Zhao, Chenchen Jing, Yufei Yin,

Organizations: State Key Lab of CAD & CG, Zhejiang University, China · Zhejiang University of Technology · Hangzhou Dianzi University, China · Xiaomi Corporation, China

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.

Figures & tables

Figure 1: We propose Metric-Bench, a benchmark inspired by human perception of 3D space: given the true physical measurements of a few reference objects, the model learns to reason about relative positions and perspective relationships in 3D space, thereby inferring precise metric measurements of other objects in the scene. Furthermore, building upon this benchmark, we employ a spatial-guided reinforcement fine-tuning strategy, without compromising the model’s generalizable reasoning capability, and achieves state-of-the-art performance in metric scale perception, surpassing existing multimodal large language models.

Figure 2: Data construction pipeline and statistics in Metric-Bench.

	MRA $\uparrow$						RMSE $\downarrow$	${\delta_{1}}\uparrow$
	Avg.	Size	Pos.	Dep.	Dis.	Box	RMSE $\downarrow$	${\delta_{1}}\uparrow$
Human	76.83	81.51	77.76	76.33	79.35	82.09	0.2671	0.7920
Proprietary Models (API)-Mini
Gemini-2.5-flash	33.95	34.74	31.96	61.64	18.87	29.09	0.7886	0.3254
GPT-5-mini	37.84	38.47	33.06	58.88	22.73	33.40	0.7842	0.4014
Open-source Models

Table 1: The metric estimation performance of public and our fine-tuned models on Metric-Bench. All public models are evaluated under zero-shot settings. Proprietary models and human performance are evaluated using a mini dataset, which contains 400 queries randomly sampled from the bench. Pos., Dep., Dis. are abbreviations for position, depth and distance. Length, width, height, elevation and dimension are all included in Size. The unit of RMSE is meters.

Figure 3: Performance of MetricReasoner across all categories in Metric-Bench.

Table 5

Models	ERQA $\uparrow$	RoboSpatial $\uparrow$	CountBench $\uparrow$	V $\star$ Bench $\uparrow$	BLINK $\uparrow$
Qwen3-VL-8B	42.25	53.14	0.917	61.78%	58.61%
VST-7B-RL	40.25	42.29	0.891	59.16%	43.32%
MetricReasoner	44.00	55.14	0.920	68.59%	81.82%

Table 4: General performance of spatial-specialized model VST-7B-RL, our MetricReasoner and corresponding base model Qwen3-VL-8B on ERQA, RoboSpatial, and general benchmarks, including CountBench, V

\star

Bench and BLINK.

Strategy	RoboSpatial	CountBench	V $\star$ Bench	BLINK
RL(MetricReasoner)	55.14	0.9204	68.59%	81.82%
RL w/ COT&SFT	45.14	0.9190	62.83%	34.53%
SFT	40.86	0.9093	63.35%	51.26%

Table 5: Results on RoboSpatial, CountBench, V

\star

Bench, and BLINK of different fine-tuning strategies. RL w/ COT&SFT denotes SFT on CoT-annotated data followed by reinforcement fine-tuning.

Figure 4: Qualitative results of MetricReasoner.

Figure 5: Results of open-source VLMs(InternVL3.5-38B and Qwen3-VL-32B) on Metric-Bench.

Figure 6: Visualization of MetricReasoner on RoboTwin2.0. Points on images are the projection of the predicted results.

Reward	MRA $\uparrow$	RMSE $\downarrow$	${\delta_{1}}\uparrow$
BP	48.59	0.5323	0.4705
BP-5	46.14	0.5685	0.4290
BP-6	48.88	0.5545	0.4800
BP-8	47.69	0.5367	0.4611

Table 6: BP reward supplementary experiments.

VLM Reasoning

Vision-language model reasoning studies how multimodal systems use visual evidence and language to answer questions, plan, and explain.

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Visual Language Models (VLMs) have increasingly become the main paradigm for understanding indoor scenes, but they still struggle with metric and spatial reasoning. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, inherently coupling perception and reasoning. In this paper, we investigate whether decoupling perception and reasoning leads to improved spatial reasoning. We propose an agentic framework for static 3D indoor scene reasoning that grounds an LLM in an explicit 3D scene graph (3DSG). Rather than ingesting videos directly, each scene is represented as a persistent 3DSG constructed by a dedicated perception module. To isolate reasoning performance, we instantiate the 3DSG from ground-truth annotations. The agent interacts with the scene exclusively through structured geometric tools that expose fundamental properties such as object dimensions, distances, poses, and spatial relationships. The results we obtain on the static split of VSI-Bench provide an upper bound under ideal perceptual conditions on the spatial reasoning performance, and we find that it is significantly higher than previous works, by up to 16%, without task specific fine-tuning. Compared to base VLMs, our agentic variant achieves significantly better performance, with average improvements between 33% to 50%. These findings indicate that explicit geometric grounding substantially improves spatial reasoning performance, and suggest that structured representations offer a compelling alternative to purely end-to-end visual reasoning.

Abstract

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