Update README.md

README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+---
+
+# Structured3D-SpatialLM Dataset
+
+Structured3D dataset preprocessed in SpatialLM format for layout estimation with LLMs.
+
+## Overview
+
+This dataset is derived from [Structured3D](https://structured3d-dataset.org/) **3,500 synthetic house designs** created by professional designers, preprocessed and formatted specifically for [SpatialLM](https://github.com/manycore-research/SpatialLM) training.
+
+Point clouds and layouts are derived from the [RoomFormer](https://github.com/ywyue/RoomFormer/tree/main/data_preprocess) data preprocessing script.
+
+## Data Extraction
+
+Point clouds and layouts are compressed in zip files. To extract the files, run the following script:
+
+```bash
+cd structured3d-spatiallm
+chmod +x extract.sh
+./extract.sh
+```
+
+## Dataset Strucutre
+
+```bash
+structured3d-spatiallm/
+├── structured3d_train.json # Training conversations
+├── structured3d_test.json # Test conversations
+├── dataset_info.json # Dataset metadata
+├── split.csv # Train/val split mapping
+├── pcd/ # Point cloud data
+│ └── .ply
+├── layout/ # Scene layout annotations
+│ └── .txt
+└── extract.sh # Extraction script
+```
+
+The `structured3d_train.json` and `structured3d_test.json` dataset follows the **SpatialLM format** with ShareGPT-style conversations:
+
+```json
+{
+  "conversations": [
+    {
+      "from": "human",
+      "value": "<point_cloud>Detect walls, doors, windows. The reference code is as followed: ..."
+    },
+    {
+      "from": "gpt",
+      "value": "<|layout_s|>wall_0=...<|layout_e|>"
+    }
+  ],
+  "point_clouds": ["pcd/scene_id.ply"]
+}
+```
+
+## License
+
+This dataset is derived from [Structured3D](https://github.com/bertjiazheng/Structured3D) dataset. Please refer to the original dataset's license terms for usage restrictions.
+
+## Citation
+
+If you use this dataset in your research, please cite the original Structured3D paper:
+
+```bibtex
+@inproceedings{Structured3D,
+  title     = {Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling},
+  author    = {Jia Zheng and Junfei Zhang and Jing Li and Rui Tang and Shenghua Gao and Zihan Zhou},
+  booktitle = {Proceedings of The European Conference on Computer Vision (ECCV)},
+  year      = {2020}
+}
+```