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README.md

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+# SURPRISE3D Dataset
+
+📄 **Paper**: [SURPRISE3D: A Dataset for Spatial Understanding and Reasoning in Complex 3D Scenes](https://huggingface.co/papers/2507.07781)
+
+🔗 **arXiv**: [arxiv:2507.07781](https://arxiv.org/abs/2507.07781)
+
+💻 **Code**: [GitHub Repository](https://github.com/liziwennba/SUPRISE)
+
+## Dataset Description
+
+SURPRISE3D is a novel dataset designed to evaluate language-guided spatial reasoning segmentation in complex 3D scenes. As detailed in our [paper](https://huggingface.co/papers/2507.07781), this dataset addresses the critical gap in current 3D vision-language research where existing datasets often mix semantic cues with spatial context.
+
+### Key Features:
+- **200k+ vision-language pairs** across 900+ detailed indoor scenes from ScanNet++ v2
+- **2.8k+ unique object classes**
+- **89k+ human-annotated spatial queries** crafted without object names to mitigate shortcut biases
+- Comprehensive coverage of spatial reasoning skills including:
+  - Relative position reasoning
+  - Narrative perspective understanding
+  - Parametric perspective analysis
+  - Absolute distance reasoning
+
+## Citation
+
+If you use SURPRISE3D in your research, please cite our paper:
+
+```bibtex
+@article{huang2025surprise3d,
+  title={SURPRISE3D: A Dataset for Spatial Understanding and Reasoning in Complex 3D Scenes},
+  author={Huang, Jiaxin and Li, Ziwen and Zhang, Hanlve and Chen, Runnan and He, Xiao and Guo, Yandong and Wang, Wenping and Liu, Tongliang and Gong, Mingming},
+  journal={arXiv preprint arXiv:2507.07781},
+  year={2025}
+}