Add paper link, GitHub link, and task metadata

README.md

@@ -1,3 +1,33 @@
----
-license: cc-by-nc-nd-4.0
----
+---
+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}
+}
+```