G2VLM-2B-MoT

Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning

We present G2VLM, a geometry grounded vision-language model proficient in both spatial 3D reconstruction and spatial understanding tasks. For spatial reasoning questions, G2VLM can natively predict 3D geometry and employ interleaved reasoning for an answer.

This repository hosts the model weights for G2VLM. For installation, usage instructions, and further documentation, please visit our GitHub repository.

🧠 Method

G2VLM is a unified model that integrates both a geometric perception expert for 3D reconstruction and a semantic perception expert for multimodal understanding and spatial reasoning tasks. All tokens can do shared multi-modal self attention in each transformer block.

License

G2VLM is licensed under the Apache 2.0 license.

✍️ Citation

@article{hu2025g2vlmgeometrygroundedvision,
      title={G$^2$VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning}, 
      author={Wenbo Hu and Jingli Lin and Yilin Long and Yunlong Ran and Lihan Jiang and Yifan Wang and Chenming Zhu and Runsen Xu and Tai Wang and Jiangmiao Pang},
      year={2025},
      eprint={2511.21688},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.21688}, 
}