README.md

---
license: cc-by-sa-4.0
language:
- en
tags:
- navigation
- scene
size_categories:
- 100B<n<1T
extra_gated_prompt: >-
  ### Scene-N1 COMMUNITY LICENSE AGREEMENT

  Scene-N1 Release Date: July 26, 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/).
extra_gated_fields:
  First Name: text
  Last Name: text
  Email: text
  Country: country
  Affiliation: text
  Phone: text
  Job title:
    type: select
    options:
    - Student
    - Research Graduate
    - AI researcher
    - AI developer/engineer
    - Reporter
    - Other
  Research interest: text
  geo: ip_location
  By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the InternData Privacy Policy: checkbox
extra_gated_description: >-
  The information you provide will be collected, stored, processed and shared in
  accordance with the InternData Privacy Policy.
extra_gated_button_content: Submit
---

<!-- <div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/64e6d9d229a548f66aff6e5b/y9cRIMFKBcY1qsxJ58y7Y.jpeg"/>
</div> -->

# SceneData-N1

<!-- 
## 🔑 Key Features

- **Unified Format for Different Benchmarks**
  
  InternData-N1 consolidates three subsets—VLN-CE, VLN-PE, and VLN-N1—into the mainstream LeRobot (v2.1) format, facilitating convenient usage across different systems and diverse benchmarks.

- **Diverse Data Covering Different Embodiments, Tasks, and Scenes**

  InternData-N1 offers diversity through its foundation of 3,000+ scene assets, extensive randomization across different robot embodiments and viewpoints, and rephrased instructions generated by LLMs for common navigation tasks.

- **High Quality Through Effective Generation and Filtering**

  InternData-N1 ensures high quality by employing effective data generation strategies (producing smooth and safe trajectories) and rigorous filtering (excluding samples with very few reference objects). This results in state-of-the-art performance for models trained on it, such as InternVLA-N1.

## 📅 TODO List

- [x] **InternData-N1 subsets**: 2.8k+ VLN-PE, 150k+ VLN-CE, 6k+ VLN-N1 episodes
- [ ] **Release 200k+ VLN-N1** (in 2 weeks)
- [ ] **VLN-CE v1 -> v1.3** (in one month)

## 📋 Table of Contents

- [InternData-N1](#interndata-n1)
  - [🔑 Key Features](#-key-features)
  - [📅 TODO List](#-todo-list)
  - [📋 Table of Contents](#-table-of-contents)
  - [🔥 Get Started](#-get-started)
    - [Download the Dataset](#download-the-dataset)
  - [Dataset Structure](#dataset-structure)
    - [Scene Data Assets](#scene-data-assets)
    - [Core Dataset Structure](#core-dataset-structure)

## 🔥 Get Started

### Download the Dataset

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

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

# When prompted for a password, use an access token with read permissions.
# Generate one from your settings: https://huggingface.co/settings/tokens
git clone https://huggingface.co/datasets/InternRobotics/InternData-N1

# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/InternRobotics/InternData-N1

``` -->
## Dataset Structure


```
scene_data/
├── mp3d_pe/ 
├── mp3d_n1/
├── mp3d_ce /
├── n1_eval_scenes/ 
│   ├── Materials
│   ├── SkyTexture
│   ├── internscenes_home
│   ├── internscenes_commercial 

```

-   `scene_data/mp3d_pe/`: Improved Matterport3D scene assets for VLN-PE benchmark.
-   `scene_data/mp3d/scans/`: Base Matterport3D scans used for generating N1 trajectory data.
-   `scene_data/mp3d/tasks/`: Matterport3D scene assets for VLN-CE benchmark.
-   `scene_data/n1_eval_scenes/`: Scene assets for N1 benchmark

> **Note**: The original scene datasets can be obtained from [Matterport3D](https://niessner.github.io/Matterport/).




# License and Citation
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/). Please consider citing our project if it helps your research.

```BibTeX
@misc{interndata_n1,
  title={InternData-N1 Dataset},
  author={InternData-N1 Dataset contributors},
  howpublished={\url{https://huggingface.co/datasets/InternRobotics/Scene-N1}},
  year={2025}
}
```