---
license: apache-2.0
task_categories:
- video-text-to-text
tags:
- 3d
- spatial-intelligence
- vlm
- visual-grounding
---

# SpaceSpan Dataset

SpaceSpan is a large-scale dataset curated for the training and evaluation of 3D vision-language models (VLMs), specifically introduced in the paper [Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment](https://huggingface.co/papers/2605.08064).

[**Project Page**](https://wzzheng.net/Proxy3D) | [**GitHub Repository**](https://github.com/Spacedreamer2384/Proxy3D)

## Dataset Description

The SpaceSpan dataset is designed to help VLMs develop spatial intelligence through 3D proxy representations. It incorporates heterogeneous visual information with a unified data format, enabling multi-stage training for skills ranging from simple image-text alignment to complex 3D spatial reasoning, 3D visual question answering (VQA), and visual grounding.

The dataset includes approximately **318K samples** used across four progressive training stages.

## Dataset Structure

The repository typically includes the following components used for the Proxy3D training pipeline:
- **Training Instructions**: JSON files for stages 1 through 4 (e.g., `stage_4_train_318K.json`).
- **Embeddings**: Pre-computed vision embeddings for efficiency.
- **Geometric Data**: Pointmaps and camera poses for 3D reconstruction and scene representation.

For evaluation annotations, please refer to the [Proxy3D-annotations](https://huggingface.co/datasets/Spacewanderer8263/Proxy3D-annotations) repository.

## Citation

If you use this dataset in your research, please cite the following paper:

```bibtex
@article{proxy3d2026,
  title={Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment},
  author={Jiang, Jerry and Sun, Haowen and Gudovskiy, Denis and Nakata, Yohei and Okuno, Tomoyuki and Keutzer, Kurt and Zheng Wenzhao},
  journal={arXiv preprint arXiv:2605.08064},
  year={2026}
}
```