Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models

• URL: http://arxiv.org/abs/2605.29074v1
• Date: Wed, 27 May 2026 20:28:56 GMT
• Title: Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models
• Abstract summary: Embodied3DBench is a robot-centric benchmark targeting low-level spatial intelligence in 3D environments.<n>The benchmark spans 12 subcategories and contains over 21k high-quality question-answer pairs.
• Score: 50.6415287154632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Are current Vision Language Models (VLMs) ready to comprehend and reason about complex embodied interactions in 3D environments? We introduce Embodied3DBench, a robot-centric benchmark targeting low-level spatial intelligence in embodied 3D environments. To systematically evaluate these foundational perceptual capabilities, the benchmark includes 6 task categories divided into two core groups: Spatial Structural Understanding (Grounding, Spatial Relation Prediction, and Multi-view Correspondence) and Interaction-Oriented Perception (Affordance Prediction, Grasp Point Prediction, and Trajectory Prediction). The benchmark spans 12 subcategories and contains over 21k high-quality question-answer pairs. We evaluate 13 state-of-the-art models, and the results show that while current models exhibit relatively strong high-level spatial reasoning, such as understanding object-to-object positional relations, they remain fragile in interaction-oriented perception, highlighting a significant lack of robust 3D-aware interaction priors. To actively bridge this capability gap revealed by our benchmark, we further synthesize a large-scale training dataset comprising 1.3M QA pairs. Notably, fine-tuning on this dataset yields significant improvements in low-level spatial intelligence. Ultimately, Embodied3DBench fills a critical gap by providing both a systematic evaluation framework and a scalable data solution, setting a clear target for the development of interaction-aware multimodal systems.

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