Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918528802619392 |
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| author | Zhang, Jiyao Zhang, Mingxu Peng, Yitong Liu, Haoxuan Wang, Chenshuo Long, Yuxing Huang, Haoyang Li, Dongjiang Duan, Nan Shen, Hui Dong, Hao |
| author_facet | Zhang, Jiyao Zhang, Mingxu Peng, Yitong Liu, Haoxuan Wang, Chenshuo Long, Yuxing Huang, Haoyang Li, Dongjiang Duan, Nan Shen, Hui Dong, Hao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_29074 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models Zhang, Jiyao Zhang, Mingxu Peng, Yitong Liu, Haoxuan Wang, Chenshuo Long, Yuxing Huang, Haoyang Li, Dongjiang Duan, Nan Shen, Hui Dong, Hao Computer Vision and Pattern Recognition Robotics 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. |
| title | Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2605.29074 |