metadata
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?
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:
@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}
}