English(EN) When Depth Lies: Benchmarking Vision-Language Models on Mirror-Induced RGB-D Ambiguity

新的MIRA-Bench揭示视觉语言模型在镜面反射方面存在困难

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了MIRA-Bench，这是一个旨在测试视觉语言模型（VLMs）在涉及镜子场景中的空间推理能力的新基准。现有的基准通常忽略了反射引入的复杂性，在反射中，深度数据可能会失真，而视觉外观保持不变。MIRA-Bench包含来自各种真实世界场景的1000多张RGB-D图像，并标注了反射表面掩码和物体反射。对最先进的VLMs的评估显示，在识别反射、将物体与其真实对应物匹配以及进行准确的空间推理方面存在重大缺陷，深度数据甚至会降低性能。 AI

影响突出了VLM空间推理中的一个关键差距，可能影响机器人和增强现实应用。

排序理由该集群描述了一个用于评估AI模型的新基准和数据集，该数据集在一篇学术论文中发表。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Hao Yin, Tianchen Guo, Heming Du, Yan Ke, Yanbin Liu, Xin Yu · 2026-11-02 05:00

When Depth Lies: Benchmarking Vision-Language Models on Mirror-Induced RGB-D Ambiguity

arXiv:2610.41651v1 Announce Type: new Abstract: Vision-language models (VLMs) have made significant progress in spatial reasoning, particularly by integrating RGB images with depth data, which is often assumed to faithfully reflect physical distances. However, mirrors violate thi…

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When Depth Lies: Benchmarking Vision-Language Models on Mirror-Induced RGB-D Ambiguity

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