MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs
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.
- ▪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.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2607.08970 (cs) [Submitted on 9 Jul 2026] Title:MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs Authors:Hantao Zhang, Jinru Sui, Ed Li, Dirk Bergemann, Zhuoran Yang View a PDF of the paper titled MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs, by Hantao Zhang and 4 other authors View PDF HTML (experimental) 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.
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