MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs
MultiView-Bench is a diagnostic benchmark designed to evaluate vision-language models' ability to integrate observations across multiple viewpoints into a coherent, world-centric 3D mental model. Current VLMs excel at single-view 2D tasks but struggle with 3D spatial relations and cross-view aggregation. The authors propose ViewNavigator, a multi-agent framework that actively selects informative viewpoints and fuses multi-view evidence, achieving 3-5x performance improvements on the benchmark.
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[Submitted on 9 Jul 2026]
Title:MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs
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Abstract:Recent benchmarks for VLMs largely assess single- or limited-view perception, leaving untested the core cognitive ability to integrate observations across viewpoints into a coherent, world-centric (allocentric) 3D mental model. We introduce MultiView-Bench, a diagnostic benchmark expressly designed to evaluate multi-view integration for holistic 3D scene comprehension. Unlike existing datasets that focus on pixel-level mapping or camera-relative navigation, MultiView-Bench requires models to decouple object positioning from transient perspectives and ground them in a fixed global coordinate system. This capability serves as a prerequisite for VLMs before being deployed for downstream tasks such as mechanical part assembly. Our systematic evaluation of frontier VLMs reveals consistent failure modes: strong performance on 2D planar relations from a single image, but marked difficulty with 3D spatial relations and with aggregating information across views. We further identify biases in VLMs, such as struggles with unconventional axis directions and sensitivity to object colorways and texture variations. Acknowledging these limitations, we propose ViewNavigator, a multi-agent framework that actively selects informative viewpoints, perceives, and fuses multi-view evidence, improving diverse base models on MultiView-Bench even under a strict budget-matched comparison (and by 3-5x for the full agent).
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.08970 [cs.CV]
(or arXiv:2607.08970v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.08970
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hantao Zhang [view email] [v1] Thu, 9 Jul 2026 22:22:42 UTC (13,767 KB)
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