metadata
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
base_model: Qwen/Qwen2.5-VL-3B-Instruct
tags:
- 3d
- spatial-reasoning
- vlm
- qwen2.5-vl
3DThinker-Mindcube
This repository contains the stage 1 model checkpoint for 3DThinker, as presented in the paper Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views.
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.
- Paper: Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
- Code: GitHub - zhangquanchen/3DThinker
Introduction
- 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}
}