Do Vision-Language Models Understand 3D Scenes or Just Catalogue Objects?

Animesh Maheshwari; Divyansh Sahu; Nishit Verma

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arXiv · 2605.20448

Do Vision-Language Models Understand 3D Scenes or Just Catalogue Objects?

Animesh Maheshwari·Divyansh Sahu·Nishit Verma

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Abstract

Vision-language models reliably name objects in a scene, but do they represent the 3D layout those objects inhabit? We introduce a 3,034-sample human-curated benchmark targeting three components of spatial understanding: depth-ordered occlusion (probed via three independent counterfactual operationalisations), optical-geometry inference over visible reflections, and volumetric rearrangement planning. Six frontier and open-weight VLMs, scored by trained annotators on 18,204 responses with no LLM-as-judge, reveal a sharp dissociation: models that plan rearrangements over visible layouts at 53--97% accuracy and rarely violate collision constraints fall to 6--45% on occlusion and below 7% on reflections. An embodied-reasoning model reproduces the same profile. White-box analysis on Qwen3-VL-8B-Thinking localises the failure to the visual-token merger: spatial information recoverable throughout the vision encoder becomes inaccessible after token compression and only stabilises again when clean post-merger activations are patched into the language decoder.

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Animesh Maheshwari, Divyansh Sahu, Nishit Verma. 2026-06-18. Do Vision-Language Models Understand 3D Scenes or Just Catalogue Objects?. https://arxiv.org/abs/2605.20448

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