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license: apache-2.0
task_categories:
- video-text-to-text
tags:
- 3D
- vision-language
- spatial-intelligence
---
# SpaceSpan Dataset
SpaceSpan is a large-scale dataset curated for aligning 3D proxy representations with Vision-Language Models (VLMs), introduced in the paper [Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment](https://huggingface.co/papers/2605.08064).
The dataset incorporates heterogeneous visual information into a unified format to support multi-stage training for developing spatial intelligence. It enables models to progress from simple image-text alignment to complex 3D reasoning tasks, such as 3D visual question answering (VQA) and visual grounding.
[**Project Page**](https://wzzheng.net/Proxy3D) | [**GitHub**](https://github.com/Spacedreamer2384/Proxy3D) | [**Paper**](https://huggingface.co/papers/2605.08064)
## Dataset Description
The SpaceSpan dataset (specifically the SpaceSpan-318K version) supports four progressive training stages:
- **Stage 1**: Initial spatial alignment.
- **Stage 2-3**: Intermediate spatial reasoning development.
- **Stage 4**: Full-scale 3D reasoning.
### Directory Structure
Based on the official repository, the dataset is typically organized as follows:
```bash
data/ # Training and inference data
├── icon_image_embeds_qwen25.pt
├── number_image_embeds_qwen25.pt
├── stage_1_train.json
├── stage_2_train.json
├── stage_3_train.json
├── stage_4_train_318K.json
├── pointmaps_wo_markers
├── poses
└── ...
```
## Citation
If you find this dataset useful for 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}
}
``` |