Think3D: Thinking with Space for Spatial Reasoning
Zaibin Zhang, Yuhan Wu, Lianjie Jia, Yifan Wang, Zhongbo Zhang, Yijiang Li, Binghao Ran, Fuxi Zhang, Zhuohan Sun, Zhenfei Yin, Lijun Wang, Huchuan Lu

TL;DR
Think3D introduces an interactive 3D reasoning framework for vision-language models, significantly enhancing their spatial understanding and reasoning capabilities through tool integration and reinforcement learning.
Contribution
It presents a novel framework that enables active 3D spatial reasoning in VLMs, including a plug-in for large models and a reinforcement learning approach for smaller models.
Findings
Performance gains of +7.8% on BLINK Multi-view and MindCube
Performance improvement of +4.7% on VSI-Bench
Reinforcement learning boosts small model performance from +0.7% to +10.7%
Abstract
While contemporary Vision-Language Models (VLMs) excel at 2D visual understanding, they remain constrained by a passive, 2D-centric paradigm that severely limits genuine 3D spatial reasoning. To bridge this gap, we introduce Think3D, a novel framework that equips VLM agents with interactive, 3D chain-of-thought reasoning capabilities. By integrating a suite of 3D manipulation tools, Think3D transforms passive perception into active spatial exploration, closely mirroring human geometric reasoning. We demonstrate that Think3D acts as a highly effective zero-shot plug-in for state-of-the-art closed-source models (e.g., GPT-4.1, Gemini 2.5 Pro), yielding absolute performance gains of +7.8% on BLINK Multi-view and MindCube, and +4.7% on VSI-Bench. Furthermore, to optimize tool-use in smaller open-weight models, we propose Think3D-RL, a reinforcement learning paradigm designed to autonomously…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Robot Manipulation and Learning · Reinforcement Learning in Robotics
