3DThinker is a framework that enables Vision-Language Models (VLMs) to exploit geometric information within images for 3D spatial reasoning, simulating human-like spatial imagination without requiring explicit 3D prior inputs or labeled 3D training data.
The model was trained on Mindcube_Train and tested on MindCube-Tiny.
This model corresponds to stage 1 training (supervised alignment of 3D latents) of Qwen2.5-3B-VL.
Note that Tab. 2 in the paper is trained on a different training data configuration.
Bibtex
If you find 3DThinker helpful for your work, please cite:
@article{chen2025think,
title={Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views},
author={Chen, Zhangquan and Zhang, Manyuan and Yu, Xinlei and Luo, Xufang and Sun, Mingze and Pan, Zihao and Feng, Yan and Pei, Peng and Cai, Xunliang and Huang, Ruqi},
journal={arXiv preprint arXiv:2510.18632},
year={2025}
}
