README.md

---
license: cc-by-4.0
task_categories:
- robotics
language:
- en
size_categories:
- 10B<n<100B
---
# EmbodiedSplat Dataset & Checkpoints

[![Paper](https://img.shields.io/badge/Paper-ICCV%202025-blue)](https://gchhablani.github.io/embodied-splat/)

High-fidelity 3D scene reconstructions, navigation episodes, and policy checkpoints  
released with the **EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device** (ICCV 2025) paper.

This dataset supports research on sim-to-real robot navigation using 3D Gaussian Splatting (DN-Splatter) and low-effort iPhone LiDAR captures.

---

## Repository Structure
```text
embodied-splat/
├── ckpts/
├── datasets/
│   └── pointnav/
│       ├── pointnav_dn_splatter/
│       ├── pointnav_dn_splatter_2/
│       ├── pointnav_hm3d_stretch/
│       ├── pointnav_hssd/
│       ├── pointnav_mushroom_dn_splatter/
│       ├── pointnav_polycam_mesh/
│       └── pointnav_polycam_mesh_2/
├── fine_tune_ckpts/
│   ├── hm3d_fine_tuned_ckpts/
│   └── hssd_fine_tuned_ckpts/
├── grad_lounge/
└── scene_datasets/
    ├── mushroom/
    │   └── dn_splatter/
    └── polycam_data/
        ├── dn_splatter/
        ├── dn_splatter_2/
        ├── polycam_mesh/
        └── polycam_mesh_2/
```


### `ckpts/`
Pre-trained **PointNav** policy checkpoints:

* **hm3d_ckpt_204.pth** – trained on the HM3D dataset  
* **hssd_ckpt_332.pth** – trained on the HSSD dataset  

Use these as starting points for fine-tuning on custom scenes.

---

### `datasets/pointnav/`
Habitat-Sim pointNav **episode datasets** used in the paper:

* **HM3D** episodes  
* **HSSD** episodes  
* **MuSHRoom** dataset episodes  
* Episodes for our **custom DN-Splatter** and **Polycam** meshes
  * `castleberry`  → **conf_b** in the paper  
  * `clough_classroom` → **classroom** in the paper  
  * `grad_lounge` → **lounge** in the paper  
  * `piedmont` → **conf_a** in the paper  
  * `polycam_mesh_2/` and `dn_splatter_2/` → **coda_conference_room** (**conf_c** in the supplementary material)

These provide navigation tasks (start/goal poses, goals, etc.) for training and evaluation.

---

### `fine_tune_ckpts/`
Fine-tuned policy checkpoints corresponding exactly to the evaluation results reported in the paper:

* **hm3d_fine_tuned_ckpts/**  
  * Policies fine-tuned **from the HM3D pre-trained model** on each of the **5 scenes** released in the paper + supplementary material.

* **hssd_fine_tuned_ckpts/**  
  * Policies fine-tuned **from the HSSD pre-trained model** on each of the **4 scenes** released in the paper.

These checkpoints directly reproduce the quantitative results shown in the main and supplementary text.

---

### `grad_lounge/`
Checkpoints used for **real-world robot navigation** in the **“lounge” scene**  
described in the paper’s real-robot experiments.

---

### `scene_datasets/`
3D scene reconstructions and meshes.

* **mushroom/** – DN-Splatter reconstructions of the [MuSHRoom dataset](https://xuqianren.github.io/publications/MuSHRoom/).  
* **polycam_data/** – Our own Polycam captures and corresponding DN-Splatter reconstructions:
  * `castleberry`  → **conf_b** in the paper  
  * `clough_classroom` → **classroom** in the paper  
  * `grad_lounge` → **lounge** in the paper  
  * `piedmont` → **conf_a** in the paper  
  * `polycam_mesh_2/` and `dn_splatter_2/` → **coda_conference_room** (**conf_c** in the supplementary material)

Each subfolder contains:
* **Polycam exported meshes** (`.glb`)  
* **DN-Splatter reconstructed meshes** (`.glb`)

---

## Usage

These resources can be used to:
* Train navigation agents in [Habitat-Sim](https://aihabitat.org/) or similar simulators.
* Reproduce all **sim-to-real** experiments from the paper.
* Fine-tune your own navigation policies on high-fidelity reconstructions.

**Please cite our paper if you use this dataset or checkpoints**:
```bibtex
@inproceedings{chhablani2025embodiedsplat,
  title={EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device},
  author={Gunjan Chhablani and Xiaomeng Ye and Muhammad Zubair Irshad and Zsolt Kira},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2025}
}
```


---

## License

All files are released under the **CC-BY-4.0** license.  
You are free to use and modify the data with appropriate attribution.

---

## Contact

For questions or collaboration inquiries, please reach out through the project page:  
➡️ [https://gchhablani.github.io/embodied-splat/](https://gchhablani.github.io/embodied-splat/)

---
license: cc-by-4.0
---