Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
Zhangquan Chen, Manyuan Zhang, Xinlei Yu, Xufang Luo, Mingze Sun, Zihao Pan, Xiang An, Yan Feng, Peng Pei, Xunliang Cai, Ruqi Huang

TL;DR
3DThinker is a novel framework that enables 3D spatial reasoning from limited views in vision-language models without requiring explicit 3D data, improving performance on spatial tasks by leveraging geometric information during reasoning.
Contribution
It introduces the first method to perform 3D mental reasoning in VLMs without 3D priors or labeled 3D data, using a two-stage training process.
Findings
Outperforms strong baselines on multiple benchmarks.
Effectively exploits geometric information during reasoning.
Unifies 3D representations into multimodal reasoning.
Abstract
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Spatial Cognition and Navigation · Constraint Satisfaction and Optimization
