Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limited views remains a significant challenge. Previous reasoning methods typically rely on pure text (e.g., topological cognitive maps) or on 2D visual cues. However, their limited representational capacity hinders performance in specific tasks that require 3D spatial imagination. To address this limitation, we propose 3DThinker, a framework that can effectively exploits the rich geometric information embedded within images while reasoning, like humans do. Our framework is the first to enable 3D mentaling during reasoning without any 3D prior input, and it does not rely on explicitly labeled 3D data for training. Specifically, our training consists of two stages. First, we perform supervised training to align the 3D latent generated by VLM while reasoning with that of a 3D foundation model (e.g., VGGT). Then, we optimize the entire reasoning trajectory solely based on outcome signals, thereby refining the underlying 3D mentaling. Extensive experiments across multiple benchmarks show that 3DThinker consistently outperforms strong baselines and offers a new perspective toward unifying 3D representations into multimodal reasoning. Our code is available at https://github.com/zhangquanchen/3DThinker.
Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 66 of its 98 references that are in the map. First posted on arXiv 21 Oct 2025.
Builds on
66 of 98 references.
Closest ancestors · 15
Same direction, newest first
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- 20243DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark10 Dec
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Other references · 51
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50 most cited of 51 All 51 (JSON)
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Citing papers per year: 2025: 7; 2026: 51.
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- 2026Chain of Spatial Thoughts: Modality-Agnostic Spatial Grounding for Vision Language Models10 Aug 2026
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