M3DBench: Let's Instruct Large Models with
Multi-modal 3D Prompts

💡Project Page • 📃Arxiv Paper • 🗂Dataset • 📖Citation

🏃 Intro M3DBench

M3DBench introduces a comprehensive 3D instruction-following dataset with support for interleaved multi-modal prompts, covering a variety of fundamental abilities in real-world 3D environments. Furthermore, M3DBench provides a new benchmark to assess large models across 3D vision-centric tasks.

Abstract

Recently, 3D understanding has become popular to facilitate autonomous agents to perform further decisionmaking. However, existing 3D datasets and methods are often limited to specific tasks. On the other hand, recent progress in Large Language Models (LLMs) and Multimodal Language Models (MLMs) have demonstrated exceptional general language and imagery tasking performance. Therefore, it is interesting to unlock MLM’s potential to be 3D generalist for wider tasks. However, current MLMs’ research has been less focused on 3D tasks due to a lack of large-scale 3D instruction-following datasets. In this work, we introduce a comprehensive 3D instructionfollowing dataset called M3DBench, which possesses the following characteristics: 1) It supports general multimodal instructions interleaved with text, images, 3D objects, and other visual prompts. 2) It unifies diverse 3D tasks at both region and scene levels, covering a variety of fundamental abilities in real-world 3D environments. 3) It is a large-scale 3D instruction-following dataset with over 320k instruction-response pairs. Furthermore, we establish a new benchmark for assessing the performance of large models in understanding multi-modal 3D prompts. Extensive experiments demonstrate the effectiveness of our dataset and baseline, supporting general 3D-centric tasks, which can inspire future research.

🚩 News

• [2023/12/15] Upload paper and init the project page

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Environment

Data

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Training

Evaluation

📖 Citation

If you find our work helps, please consider starring ⭐ us and citing:

@misc{li2023m3dbench,
      title={M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts}, 
      author={Mingsheng Li and Xin Chen and Chi Zhang and Sijin Chen and Hongyuan Zhu and Fukun Yin and Gang Yu and Tao Chen},
      year={2023},
      eprint={2312.10763},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgments

Thanks to DepthContrast, Vote2Cap-DETR, OPT, Llama 2, and Vicuna. We borrow some of their codes and checkpoints.

License

This code is distributed under an MIT LICENSE. If there are any problem regarding our project, please open an issue.