SpatialRGPT-Bench-Extended

SpatialRGPT-Bench-Extended is an extension of SpatialRGPT-Bench that incorporates driving scene images from Japan. It augments object-centric QA (questions about two objects within an image) with ego-centric QA (questions about the relationship between the ego and a single object in the image). Each QA category contains 466 QA pairs. For further details, please refer to our paper: https://arxiv.org/abs/2508.10427.

🔗 Related Links

• Project Page: https://turingmotors.github.io/stride-qa
• GitHub: https://github.com/turingmotors/STRIDE-QA-Dataset
• STRIDE-QA-Dataset: https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset
• STRIDE-QA-Bench: https://huggingface.co/datasets/turing-motors/STRIDE-QA-Bench

📚 Citation

@article{cheng2024spatialrgpt,
  title={Spatialrgpt: Grounded spatial reasoning in vision-language models},
  author={Cheng, An-Chieh and Yin, Hongxu and Fu, Yang and Guo, Qiushan and Yang, Ruihan and Kautz, Jan and Wang, Xiaolong and Liu, Sifei},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={135062--135093},
  year={2024}
}

@misc{strideqa2025,
      title={STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes},
      author={Keishi Ishihara and Kento Sasaki and Tsubasa Takahashi and Daiki Shiono and Yu Yamaguchi},
      year={2025},
      eprint={2508.10427},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.10427},
}

📄 License

STRIDE-QA-Bench is released under the CC BY-NC-SA 4.0.

🤝 Acknowledgements

This benchmark is based on results obtained from a project, JPNP20017, subsidized by the New Energy and Industrial Technology Development Organization (NEDO).

We would like to acknowledge the use of the following open-source repositories:

• SpatialRGPT for building dataset generation pipeline
• SAM 2.1 for segmentation mask generation
• dashcam-anonymizer for anonymization

🔏 Privacy Protection

To ensure privacy protection, human faces and license plates in the images were anonymized using the Dashcam Anonymizer.