Add dataset card and link to paper

README.md

@@ -1,3 +1,44 @@
----
-license: apache-2.0
----
+---
+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}
+}
+```