November 9, 2025Open Access

Struct2D: A Perception-Guided Framework for Spatial Reasoning in MLLMs

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Spatial reasoning abilities are enhanced through structured 2D inputs, indicating effective interaction with 3D environments.
In-depth zero-shot analysis demonstrated that closed-source MLLMs can handle route planning and dense captioning tasks.
Struct2D utilizes a perception-guided framework to analyze spatial reasoning, relying on 2D representations without 3D data.
The findings highlight the potential for bridging perception and language reasoning in large language models, demanding further exploration.

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Implication

Zero-shot analysis reveals strong spatial reasoning abilities in MLLMs using structured 2D inputs, suggesting effective route planning and dense captioning capabilities.

Cite This Study

Zhu et al. (2025) studied this question.

synapsesocial.com/papers/690fdcdaf60c54d04ea37f50 https://doi.org/10.48550/arxiv.2506.04220

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Implication

Zero-shot analysis reveals strong spatial reasoning abilities in MLLMs using structured 2D inputs, suggesting effective route planning and dense captioning capabilities.

Struct2D: A Perception-Guided Framework for Spatial Reasoning in MLLMs

Key Points

Discussion

Implication

Cite This Study

Struct2D: A Perception-Guided Framework for Spatial Reasoning in MLLMs

Key Points

Discussion

Implication

Cite This Study