File size: 2,263 Bytes
190cff6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ---
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}
}
``` |