Structured3D-SpatialLM Dataset

Structured3D dataset preprocessed in SpatialLM format for layout estimation with LLMs.

Overview

This dataset is derived from Structured3D 3,500 synthetic house designs created by professional designers, preprocessed and formatted specifically for SpatialLM training.

Point clouds and layouts are derived from the RoomFormer data preprocessing script.

Data Extraction

Point clouds and layouts are compressed in zip files. To extract the files, run the following script:

cd structured3d-spatiallm
chmod +x extract.sh
./extract.sh

Dataset Strucutre

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:

{
  "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 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:

@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}
}