Do Vision--Language Models Understand 3D Scenes or Just Catalogue Objects?
For researchers developing vision-language models, this work identifies a critical bottleneck in spatial reasoning that is not addressed by current architectures.
The paper introduces a 3,034-sample benchmark for 3D spatial understanding and tests six VLMs, finding that while they perform well on rearrangement planning (53-97% accuracy), they fail on occlusion (6-45%) and reflection tasks (<7%), with the failure localized to the visual-token merger in Qwen3-VL-8B-Thinking.
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.