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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.11442 |
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| _version_ | 1866911380427243520 |
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| author | Gao, Xiangjun Zhang, Zhensong Chen, Dave Zhenyu Xu, Songcen Quan, Long Pérez-Pellitero, Eduardo Jang, Youngkyoon |
| author_facet | Gao, Xiangjun Zhang, Zhensong Chen, Dave Zhenyu Xu, Songcen Quan, Long Pérez-Pellitero, Eduardo Jang, Youngkyoon |
| contents | We propose Map2Thought, a framework that enables explicit and interpretable spatial reasoning for 3D VLMs. The framework is grounded in two key components: Metric Cognitive Map (Metric-CogMap) and Cognitive Chain-of-Thought (Cog-CoT). Metric-CogMap provides a unified spatial representation by integrating a discrete grid for relational reasoning with a continuous, metric-scale representation for precise geometric understanding. Building upon the Metric-CogMap, Cog-CoT performs explicit geometric reasoning through deterministic operations, including vector operations, bounding-box distances, and occlusion-aware appearance order cues, producing interpretable inference traces grounded in 3D structure. Experimental results show that Map2Thought enables explainable 3D understanding, achieving 59.9% accuracy using only half the supervision, closely matching the 60.9% baseline trained with the full dataset. It consistently outperforms state-of-the-art methods by 5.3%, 4.8%, and 4.0% under 10%, 25%, and 50% training subsets, respectively, on the VSI-Bench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11442 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Map2Thought: Explicit 3D Spatial Reasoning via Metric Cognitive Maps Gao, Xiangjun Zhang, Zhensong Chen, Dave Zhenyu Xu, Songcen Quan, Long Pérez-Pellitero, Eduardo Jang, Youngkyoon Computer Vision and Pattern Recognition Artificial Intelligence We propose Map2Thought, a framework that enables explicit and interpretable spatial reasoning for 3D VLMs. The framework is grounded in two key components: Metric Cognitive Map (Metric-CogMap) and Cognitive Chain-of-Thought (Cog-CoT). Metric-CogMap provides a unified spatial representation by integrating a discrete grid for relational reasoning with a continuous, metric-scale representation for precise geometric understanding. Building upon the Metric-CogMap, Cog-CoT performs explicit geometric reasoning through deterministic operations, including vector operations, bounding-box distances, and occlusion-aware appearance order cues, producing interpretable inference traces grounded in 3D structure. Experimental results show that Map2Thought enables explainable 3D understanding, achieving 59.9% accuracy using only half the supervision, closely matching the 60.9% baseline trained with the full dataset. It consistently outperforms state-of-the-art methods by 5.3%, 4.8%, and 4.0% under 10%, 25%, and 50% training subsets, respectively, on the VSI-Bench. |
| title | Map2Thought: Explicit 3D Spatial Reasoning via Metric Cognitive Maps |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.11442 |