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README.md
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# MMSI-Bench
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This repo contains evaluation code for the paper "[MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence]"
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[**π Homepage**](https://runsenxu.com/projects/MMSI_Bench/) | [**π€ Dataset**](https://huggingface.co/datasets/RunsenXu/MMSI-Bench) | [**π Paper**] | [**π» Code**](https://github.com/OpenRobotLab/
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## πNews
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**π₯[2025-05-31]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.**
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**π₯[2025-05-30]: We released the ArXiv paper.**
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```
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from datasets import load_dataset
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print(dataset)
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```
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| Gemini-2.5-Pro--Thinking | 37.0 | Proprietary |
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| Gemini-2.5-Pro | 36.9 | Proprietary |
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| Doubao-1.5-pro | 33.0 | Proprietary |
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| Qwen2.5-VL-72B | 30.7 | Open-source |
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| NVILA-15B | 30.5 | Open-source |
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| GPT-4.1 | 30.9 | Proprietary |
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| GPT-4o | 30.3 | Proprietary |
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| Claude-3.7-Sonnet--Thinking | 30.2 | Proprietary |
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| Seed1.5-VL | 29.7 | Proprietary |
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| InternVL2.5-8B | 28.7 | Open-source |
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| InternVL3-78B | 28.5 | Open-source |
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| InternVL2.5-78B | 28.5 | Open-source |
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| LLaVA-OneVision-72B | 28.4 | Open-source |
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| InternVL2.5-2B | 29.0 | Open-source |
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| InternVL2.5-26B | 28.0 | Open-source |
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| NVILA-8B | 28.1 | Open-source |
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| DeepSeek-VL2 | 27.1 | Open-source |
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| InternVL3-1B | 27.0 | Open-source |
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| InternVL3-9B | 26.7 | Open-source |
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| Qwen2.5-VL-3B | 26.5 | Open-source |
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| InternVL2.5-1B | 26.1 | Open-source |
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| InternVL2.5-4B | 26.3 | Open-source |
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| InternVL3-8B | 25.7 | Open-source |
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| Qwen2.5-VL-7B | 25.9 | Open-source |
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| InternVL3-
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| Llama-3.2-11B-Vision | 25.4 | Open-source |
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| π **Random Guessing** | 25.0 | Baseline |
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| LLaVA-OneVision-7B | 24.5 | Open-source |
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| DeepSeek-VL2-Tiny | 24.0 | Open-source |
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| Blind GPT-4o | 22.7 | Baseline |
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## Acknowledgment
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MMSI-Bench makes use of data from existing image datasets: [ScanNet](http://www.scan-net.org/), [nuScenes](https://www.nuscenes.org/), [Matterport3D](https://niessner.github.io/Matterport/), [Ego4D](https://ego4d-data.org/), [AgiBot-World](https://agibot-world.cn/), [DTU](https://roboimagedata.compute.dtu.dk/?page_id=36), [DAVIS-2017](https://davischallenge.org/) ,and [Waymo](https://waymo.com/open/). We thank these teams for their open-source contributions.
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- Runsen Xu: runsxu@gmail.com
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## Citation
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**BibTeX:**
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```bibtex
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```
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# MMSI-Bench
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This repo contains evaluation code for the paper "[MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence]"
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[**π Homepage**](https://runsenxu.com/projects/MMSI_Bench/) | [**π€ Dataset**](https://huggingface.co/datasets/RunsenXu/MMSI-Bench) | [**π Paper**](https://arxiv.org/pdf/2505.23764) | [**π» Code**](https://github.com/OpenRobotLab/MMSI-Bench) | [**π arXiv**](https://arxiv.org/abs/2505.23764)
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## πNews
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<!-- **π₯[2025-05-31]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.** -->
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**π₯[2025-05-30]: We released the ArXiv paper.**
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```
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from datasets import load_dataset
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mmsi_bench = load_dataset("RunsenXu/MMSI-Bench")
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print(dataset)
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```
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| Gemini-2.5-Pro--Thinking | 37.0 | Proprietary |
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| Gemini-2.5-Pro | 36.9 | Proprietary |
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| Doubao-1.5-pro | 33.0 | Proprietary |
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| GPT-4.1 | 30.9 | Proprietary |
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| Qwen2.5-VL-72B | 30.7 | Open-source |
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| NVILA-15B | 30.5 | Open-source |
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| GPT-4o | 30.3 | Proprietary |
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| Claude-3.7-Sonnet--Thinking | 30.2 | Proprietary |
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| Seed1.5-VL | 29.7 | Proprietary |
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| InternVL2.5-2B | 29.0 | Open-source |
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| InternVL2.5-8B | 28.7 | Open-source |
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| DeepSeek-VL2-Small | 28.6 | Open-source |
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| InternVL3-78B | 28.5 | Open-source |
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| InternVL2.5-78B | 28.5 | Open-source |
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| LLaVA-OneVision-72B | 28.4 | Open-source |
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| NVILA-8B | 28.1 | Open-source |
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| InternVL2.5-26B | 28.0 | Open-source |
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| DeepSeek-VL2 | 27.1 | Open-source |
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| InternVL3-1B | 27.0 | Open-source |
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| InternVL3-9B | 26.7 | Open-source |
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| Qwen2.5-VL-3B | 26.5 | Open-source |
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| InternVL2.5-1B | 26.1 | Open-source |
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| InternVL2.5-4B | 26.3 | Open-source |
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| Qwen2.5-VL-7B | 25.9 | Open-source |
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| InternVL3-8B | 25.7 | Open-source |
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| Llama-3.2-11B-Vision | 25.4 | Open-source |
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| InternVL3-2B | 25.3 | Open-source |
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| π **Random Guessing** | 25.0 | Baseline |
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| LLaVA-OneVision-7B | 24.5 | Open-source |
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| DeepSeek-VL2-Tiny | 24.0 | Open-source |
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| Blind GPT-4o | 22.7 | Baseline |
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## Acknowledgment
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MMSI-Bench makes use of data from existing image datasets: [ScanNet](http://www.scan-net.org/), [nuScenes](https://www.nuscenes.org/), [Matterport3D](https://niessner.github.io/Matterport/), [Ego4D](https://ego4d-data.org/), [AgiBot-World](https://agibot-world.cn/), [DTU](https://roboimagedata.compute.dtu.dk/?page_id=36), [DAVIS-2017](https://davischallenge.org/) ,and [Waymo](https://waymo.com/open/). We thank these teams for their open-source contributions.
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- Runsen Xu: runsxu@gmail.com
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## Citation
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```bibtex
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@article{yang2025mmsi,
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title={MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence},
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author={Yang, Sihan and Xu, Runsen and Xie, Yiman and Yang, Sizhe and Li, Mo and Lin, Jingli and Zhu, Chenming and Chen, Xiaochen and Duan, Haodong and Yue, Xiangyu and Lin, Dahua and Wang, Tai and Pang, Jiangmiao},
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journal={arXiv preprint arXiv:2505.23764},
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year={2025}
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}
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```
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