🏠 Introduction

GN0 is a unified research framework for Generation, Evaluation, and Policy Learning in Vision-and-Language Navigation (VLN). Built upon 3D Gaussian Splatting (3DGS), GN0 bridges realistic scene construction, high-fidelity embodied simulation, and navigation policy evaluation in visually grounded indoor environments.

This repository hosts the GN-Bench evaluation workflow. The current release focuses on the InteriorGS setting and provides a compact, reproducible pipeline for evaluating BAE-based navigation agents.

Highlights

• 3DGS-native navigation benchmark. GN-Bench evaluates agents directly in high-fidelity 3D Gaussian Splatting scenes.
• Unified GN0 ecosystem. The repository connects GN-Matrix data, GN-Bench simulation, and GN-BAE policy evaluation.
• InteriorGS evaluation workflow. A cleaned entry point is provided for instruction-following evaluation on InteriorGS scenes.
• Scalable episode splitting. Multi-GPU and multi-process evaluation are supported through configurable chunks.
• Lightweight metric analysis. Evaluation logs can be summarized into TL, NE, OS, SR, and SPL with a single script.

🔥 News

Time	Update
2026/07	GN-Matrix InteriorGS test trajectories released
2026/06	GN0-VLN-CE evaluation workflow released
2026/06	GN-Bench InteriorGS evaluation workflow released

📋 Table of Contents

• 🏠 Introduction
• 🔥 News
• 📦 Overview
• 📚 Getting Started
• 🧪 Evaluation
• 🧭 GN0-VLN-CE
• 🔗 Citation
• 👏 Acknowledgements

📦 Overview

🧩 GN0 Components

GN-Matrix	GN-Bench	GN-BAE
Large-scale 3DGS navigation trajectories.	Interactive benchmark and simulator for high-fidelity VLN evaluation.	Navigation foundation model for map-based and map-free policy learning.

🤗 Model Zoo & Datasets

• BAE checkpoint
• InteriorGS dataset
• GN-Matrix dataset

📚 Getting Started

Please refer to INSTALLATION.md for the complete environment setup, including PyTorch, CUDA extensions, GN-Bench-Tools, and BAE installation.

After installation, prepare datasets and checkpoints with the following layout:

GN0/
├── data/                         # Dataset files
│   ├── datasets/
│   │   └── GN_Matrix/
│   │       └── InteriorGS/
│   └── scene_datasets/
│       └── InteriorGS/
├── GN-Bench-Tools/               # Benchmark tools
└── model_zoo/
    └── bae/                      # Pretrained model weights

Run the InteriorGS evaluation:

bash eval_bae_InteriorGS.sh \
  --model-path model_zoo/bae \
  --chunks 1 \
  --procs-per-gpu 1 \
  --save-path tmp/bae_eval

Monitor evaluation progress:

watch -n 1 python analyze_results.py --path tmp/bae_eval

Terminate active evaluation workers if needed:

bash kill_bae_eval.sh

🧪 Evaluation

📊 Metrics

analyze_results.py reads JSON logs under the selected result directory and reports standard VLN metrics:

Metric	Meaning
TL	Average trajectory length
NE ↓	Navigation error
OS ↑	Oracle success
SR ↑	Success rate
SPL ↑	Success weighted by path length

🧭 GN0-VLN-CE

GN0-VLN-CE is a separate companion repository for evaluating our GN-BAE navigation model on the standard VLN-CE benchmark. It connects GN0-style policy learning with established VLN-CE evaluation protocols, while the current repository focuses on the GN-Bench InteriorGS evaluation workflow.

🔗 Citation

If GN0 is useful for your research, please cite our paper:

@article{li2026gn0,
  title={GN0: Toward a Unified Paradigm for Generation, Evaluation, and Policy Learning in Visual-Language Navigation},
  author={Li, Xinhai and Zhang, Xiaotao and Huang, Yuehao and Dong, Jiankun and Wang, Tianhang and Zhou, Sunyao and Wu, Yunzi and Sun, Chengnuo and Ge, Yunfei and Weng, Qizhen and others},
  journal={arXiv preprint arXiv:2606.03682},
  year={2026}
}

👏 Acknowledgements

GN-Bench-Tools is adapted from Habitat-Lab and customized for 3D Gaussian Splatting-based navigation. We sincerely thank:

• The Habitat-Lab developers for their foundational simulation framework.
• The InteriorGS authors for releasing their high-quality open-source dataset.
• The broader Embodied AI and 3DGS open-source communities for continuously advancing the field and making this infrastructure a reality.