---
task_categories:
- other
pretty_name: SURPRISE3D
tags:
- 3d
- spatial-reasoning
- segmentation
- vision-language
- embodied-ai
library_name: datasets
license: mit
size_categories:
- 100K<n<1M
language:
- en
---

# 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}
}