| --- |
| 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} |
| } |
| ``` |
