---
license: cc-by-nc-nd-4.0
task_categories:
- image-text-to-text
tags:
- omnidirectional-images
- mllm
- benchmark
---

# ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?

[**Paper**](https://huggingface.co/papers/2510.11549) | [**GitHub**](https://github.com/IntMeGroup/ODI-Bench)

ODI-Bench is a comprehensive benchmark specifically designed for omnidirectional image (ODI) understanding. While multi-modal large language models (MLLMs) excel at conventional 2D images, their ability to comprehend the immersive 360° × 180° environments captured by ODIs is less explored. 

The benchmark contains:
- **2,000** high-quality omnidirectional images.
- Over **4,000** manually annotated question-answering (QA) pairs.
- **10** fine-grained tasks covering both general-level and spatial-level ODI understanding.

## Citation

If you find this work useful, please cite:

```bibtex
@article{yang2025odi,
  title={ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?},
  author={Yang, Liu and Duan, Huiyu and Tao, Ran and Cheng, Juntao and Wu, Sijing and Li, Yunhao and Liu, Jing and Min, Xiongkuo and Zhai, Guangtao},
  journal={arXiv preprint arXiv:2510.11549},
  year={2025}
}
```