---
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}
}
```