metadata
license: apache-2.0
language:
- en
tags:
- robotics
- navigation
- 3dgs
- gaussian-splatting
- benchmark
NavBench-GS: 3D Gaussian Splatting Scenarios for Visual Navigation
NavBench-GS is a collection of real-world 3D Gaussian Splatting (3DGS) scenarios for benchmarking visual navigation models. Each scenario contains a reconstructed outdoor scene with paired USD meshes, 3DGS point clouds, and navigation waypoints.
Paper: S2E: From Seeing to Experiencing (ICLR 2026)
Project Page: https://vail-ucla.github.io/S2E/
Dataset Structure
NavBenchGS/
├── vid2sim_raw/ # USD mesh scenarios
│ ├── 0000/
│ │ ├── tsdf_fusion_post.usd # Reconstructed mesh (Isaac Sim compatible)
│ │ ├── tsdf_fusion_post.ply # PLY mesh
│ │ ├── config.yaml # Scenario configuration
│ │ └── point_cloud.ply # Raw point cloud
│ ├── 0001/
│ └── ...
├── vid2sim_torch/ # 3DGS point clouds (PyTorch format)
│ ├── 0000/ # gsplat-compatible 3DGS data
│ ├── 0001/
│ └── ...
└── vid2sim_starting_ending_position.json # Start/target waypoints per scenario
Usage
With S2E / URBAN-SIM (Isaac Sim)
# Clone S2E
git clone https://github.com/VAIL-UCLA/S2E.git
# Download this dataset to S2E/assets/NavBenchGS/
# Then run navigation in a scenario:
python vid2sim/main.py --scenario_id 0 --nav_mode forward --enable_cameras
Scenario Info
Each scenario has start and target positions defined in vid2sim_starting_ending_position.json:
{
"0": {"start_x": 0.0, "start_y": -20.0, "target_x": 0.0, "target_y": 2.0},
"1": {"start_x": ..., "start_y": ..., "target_x": ..., "target_y": ...},
...
}
Requirements
- NVIDIA Isaac Sim 5.x
- IsaacLab extension (3DGS camera support)
- gsplat for Gaussian Splatting rendering
Citation
@inproceedings{he2025seeing,
title={From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning},
author={Honglin He and Yukai Ma and Brad Squicciarini and Wayne Wu and Bolei Zhou},
booktitle={International Conference on Learning Representations},
year={2026}
}
License
Apache 2.0