MV-STRIDE: Enabling MLLMs to Master Multi-View Spatial Reasoning via Hierarchical Capability Modeling

Despite the rapid progress of Multimodal Large LanguageModels (MLLMs) in 2D vision-language tasks, robust multi-view spatialreasoning remains a fundamental bottleneck due to the lack of struc-tured 3D cognitive pathways in existing datasets. To address this, weintroduce MV-STRIDE, a Multi-View hierarchical SpaTial Reasoningdataset with Interdependent and Decomposed capabilitiEs. Moving be-yond x001Dat data structures, MV-STRIDE explicitly models the dependencyrelationships between foundational perception, scene understanding, andcomplex contextual reasoning, providing a coherent learning pathwayaligned with human spatial cognition. We develop a systematic QA gen-eration pipeline leveraging diverse 3D scene sources that enforces cross-view dependency constraints to prevent single-view solvability, generat-ing multi-level spatial reasoning tasks supported by cognitively groundedchain-of-thought supervision for complex inference. Extensive evalua-tions demonstrate that our multi-stage training framework based on ourhierarchical dataset achieves state-of-the-art performance across multiplespatial reasoning benchmarks, notably the multi-view oriented MMSI-Bench. Our approach enables MLLMs to maintain robust, 3D-consistentspatial reasoning across diverse viewpoints. The code and dataset areavailable at https://co1dspring.github.io/MV-STRIDE/.