构建多模态大模型地理定位评估基准，推动具身智能发展
ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
- 设计三阶段观测场景：单视角、全景、具身移动，模拟真实感知过程
- 覆盖2207张全球街景图，测试感知、空间、常识与定位推理能力
- 揭示定位能力依赖综合认知，非单一视觉识别，适合研究具身智能者
多模态大语言模型在作为具身代理方面展现出强大潜力，但具身地理定位仍因缺乏细粒度评估而未被充分探索。我们提出ERGeoBench，一个面向视觉驱动的具身地理定位诊断基准。该基准在三个渐进设置下评估模型：单视角、全景视角和具身视角，其中代理可通过连续改变偏航、俯仰和缩放主动获取观测。基准包含2,207张分布全球的街景全景图，衡量四项互补能力：基础感知、空间意识、常识推理和地理定位推理。对领先专有及开源多模态大模型的评估显示，当前模型可推断高层次地理语义，但仍难以实现精细感知操作、度量定位以及跨视角的空间一致性。进一步观察发现，地理定位与其它能力维度高度相关，表明准确定位依赖于感知、空间推理与常识推断的整合，而非孤立的视觉识别。总体而言，ERGeoBench为诊断和推进类人具身地理定位提供了统一框架。
原文摘要 · Abstract (English)
Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to the lack of fine-grained evaluation. We introduce ERGeoBench, a diagnostic benchmark for vision-driven embodied geo-localization. ERGeoBench evaluates models under three progressive settings -- single-view, panorama-view, and embodied-view -- where agents may actively acquire observations through sequential changes in yaw, pitch, and zoom. The benchmark contains 2,207 globally distributed street-view panoramas and measures four complementary capabilities: foundational perception, spatial awareness, common sense reasoning, and geo-localization reasoning. Evaluations of leading proprietary and open-source MLLMs show that current models can infer high-level geographic semantics, but still struggle with fine-grained perceptual operations, metric localization, and spatial consistency across views. We further observe that geo-localization is strongly correlated with the other capability dimensions, suggesting that accurate localization depends on integrated perception, spatial reasoning, and commonsense inference rather than isolated visual recognition. Overall, ERGeoBench provides a unified framework for diagnosing and advancing human-like embodied geo-localization. Project Page: https://kaixuewen.github.io/ERGeoBench/
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