AI RESEARCH
Cog3DMap: Multi-View Vision-Language Reasoning with 3D Cognitive Maps
arXiv CS.CV
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ArXi:2603.23023v1 Announce Type: new Precise spatial understanding from multi-view images remains a fundamental challenge for Multimodal Large Language Models (MLLMs), as their visual representations are predominantly semantic and lack explicit geometric grounding. While existing approaches augment visual tokens with geometric cues from visual geometry models, their MLLM is still required to implicitly infer the underlying 3D structure of the scene from these augmented tokens, limiting its spatial reasoning capability. To address this issue, we.