Add dataset card and metadata for SpaceSpan

Hi! I'm Niels from the Hugging Face community science team. This PR improves the dataset card for the SpaceSpan dataset (associated with the Proxy3D paper). It adds the appropriate task categories, tags, and links to the paper, project page, and code repository to make the dataset more discoverable and well-documented.

README.md

@@ -1,3 +1,54 @@
----
-license: apache-2.0
----
+---
+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}
+}
+```