metadata

pretty_name: InternScenes
size_categories:
  - n>1T
task_categories:
  - other
language:
  - en
tags:
  - Embodied-AI
  - Interactive-Scenes
  - Scene-Generation
  - Scene-Understanding
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  ### InternScenes COMMUNITY LICENSE AGREEMENT

  InternScenes Release Date: July 30, 2025 All the data and code within this
  repo are under [CC BY-NC-SA
  4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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InternScenes

InternScenes is a large-scale interactive indoor scene dataset with realistic layouts. This dataset comprises approximately 40,000 diverse scenes and 1.96M 3D objects that cover 15 common scene types and 288 object classes, which is roughly 10 times larger than existing datasets.

🔑 Key Features

InternScenes integrates a wide variety of scenes, and particularly, preserves small items for complex layouts, resolve collisions, and further incorporates interactive objects, thus ensures:

📊 Large scale: 40,000 diverse scenes including 1.96M 3D objects covering 288 object classes.
🚪 Realistic layouts: preserving massive small objects to strictly align with real-world scanned scenes.
🕹️ Interactivity: 20% interactive objects inside covering 16 common types, such as cabinets, microwaves, ovens, and fridges.

Which tasks will benefit from our dataset?

✅ 3D scene reconstruction
✅ 3D scene understanding
✅ Scene layout generation
✅ Embodied navigation

📋 Table of Contents

🔑 Key Features
⚙️ Getting Started
- Download the Dataset
- Dataset Structure
📖 TODO List
🧷 Citation
📄 License

⚙️ Getting Started

Download the Dataset

To download the full dataset, you can use the following code. If you encounter any issues, please refer to the official Hugging Face documentation.

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

# When prompted for a password, use an access token with write permissions.
# Generate one from your settings: https://huggingface.co/settings/tokens
git clone https://huggingface.co/datasets/OpenRobotLab/InternScenes

# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/OpenRobotLab/InternScenes

If you only want to download a specific dataset, such as splitaloha, you can use the following code.

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

# Initialize an empty Git repository
git init InternScenes
cd InternScenes

# Set the remote repository
git remote add origin https://huggingface.co/datasets/OpenRobotLab/InternScenes

# Enable sparse-checkout
git sparse-checkout init

# Pull the data
git pull origin main

Dataset Structure

InternScenes-Real2Sim/
|-- Assets_library/                 # Assets library of scenes
  |-- objaverse/                      # 1. Objaverse assets library    
  |-- hssd-models/                    # 2. HSSD assets library 
  |-- 3D-FUTURE-model/                # 3. 3D-FUTURE assets library
  |-- gr100/                          # 4. GRScenes-100 assets library
  |-- partNet-mobility/               # 5. PartNet-Mobility assets library
  |-- gen-assets/                     # 6. Generated assets library
|-- Layout_info/                   
  |-- scan_id/
    |-- StructureMesh/              # 3D mesh of the floor and walls
      |-- wall.glb     
    |-- layout.json                 # Layout json of the scene

The layout format is listed as follows:

[
    {
        "id": 1,
        "category": "chair",
        "model_uid": "partnet_mobility/39551",
        "bbox": [
            1.041122286614026,
            -1.2630096162069782,
            0.37856578639578786,
            0.42791932981359787,
            0.4573552539873118,
            0.7564487395312743,
            1.384006110201953,
            0.0,
            -0.0
        ]
    }
...
]

📋 TODO List

Release the InternScenes-Real2Sim v1.0.
Release trajectories of each scene.
Release the InternScenes-Real2Sim v2.0.
Release the InternScenes-Synthetic v1.0.
Release the InternScenes-Synthetic v2.0.

🧷 Citation

@inproceedings{InternScenes,
  title={InternScenes: A Large-scale Interactive Indoor Scene Dataset with Realistic Layouts},
  author={Zhong, Weipeng and Cao, Peizhou and Jin, Yichen and Li, Luo and Cai, Wenzhe and Lin, Jingli and Lyu, Zhaoyang and Wang, Tai and Dai, Bo and Xu, Xudong and Pang, Jiangmiao},
  year={2025},
  booktitle={arXiv},
}

📄License

This work is under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.