#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/gaussian_splatting" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# **Gaussian Splats Dataset**

**3D Gaussian Splatting for Real-Time Radiance Field Rendering**

**Dataset Author**: Paula Ramos<br />
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**Created Using**: [3D Gaussian Splatting Paper](https://arxiv.org/abs/2308.04079)<br />
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**Code Repository**: [GitHub - graphdeco-inria/gaussian-splatting](https://github.com/graphdeco-inria/gaussian-splatting)

# **Description**

This dataset consists of Gaussian Splats representations of different real-world scenes, created using the official 3D Gaussian Splatting method. Each scene folder contains:

```none
A reference image representing the scene.
A PLY file stored in a point_cloud_folder, containing the Gaussian Splats reconstruction.
```

## **Overview**

This dataset consists of **Gaussian Splats representations** of different real-world scenes, created using the official **3D Gaussian Splatting method**. Each scene folder contains:

- A **reference image** representing the scene.
- Two **PLY files** stored in a `point_cloud_folder`, containing the **Gaussian Splats reconstructions at iterations 7000 and 30000**.

The dataset is structured as follows:

```plaintext
FO_dataset/
 drjohnson/          # Scene Folder
    reference_image.png
    point_cloud_folder/
        reconstruction_7000.ply
        reconstruction_30000.ply
 playroom/
    reference_image.png
    point_cloud_folder/
        reconstruction_7000.ply
        reconstruction_30000.ply
 train/
    reference_image.png
    point_cloud_folder/
        reconstruction_7000.ply
        reconstruction_30000.ply
 truck/
    reference_image.png
    point_cloud_folder/
        reconstruction_7000.ply
        reconstruction_30000.ply
```

---

# **How to Use the Dataset**

## **1. Install the Required FiftyOne Plugin**

To visualize all `.ply` files using FiftyOne, download the **Gaussian Splats plugin**:

```bash
!fiftyone plugins download https://github.com/danielgural/ksplats_panel
```

## **2. Load & Visualize the Dataset with FiftyOne**

Use the following Python script to **load and explore the dataset** in FiftyOne:

```python
import fiftyone as fo
from fiftyone.utils.splats import SplatFile

# Create a FiftyOne dataset
dataset = fo.Dataset(name="splat-test", overwrite=True)

# Add samples (update paths as needed)
sample1 = fo.Sample(filepath="FO_dataset/drjohnson/reference_image.png")
sample1["splat"] = SplatFile(filepath="FO_dataset/drjohnson/point_cloud_folder/reconstruction_30000.ply")

sample2 = fo.Sample(filepath="FO_dataset/drjohnson/reference_image.png")
sample2["splat"] = SplatFile(filepath="FO_dataset/drjohnson/point_cloud_folder/reconstruction_7000.ply")

sample3 = fo.Sample(filepath="FO_dataset/playroom/reference_image.png")
sample3["splat"] = SplatFile(filepath="FO_dataset/playroom/point_cloud_folder/reconstruction_7000.ply")

sample4 = fo.Sample(filepath="FO_dataset/playroom/reference_image.png")
sample4["splat"] = SplatFile(filepath="FO_dataset/playroom/point_cloud_folder/reconstruction_30000.ply")

sample5 = fo.Sample(filepath="FO_dataset/train/reference_image.png")
sample5["splat"] = SplatFile(filepath="FO_dataset/train/point_cloud_folder/reconstruction_7000.ply")

sample6 = fo.Sample(filepath="FO_dataset/train/reference_image.png")
sample6["splat"] = SplatFile(filepath="FO_dataset/train/point_cloud_folder/reconstruction_30000.ply")

sample7 = fo.Sample(filepath="FO_dataset/truck/reference_image.png")
sample7["splat"] = SplatFile(filepath="FO_dataset/truck/point_cloud_folder/reconstruction_7000.ply")

sample8 = fo.Sample(filepath="FO_dataset/truck/reference_image.png")
sample8["splat"] = SplatFile(filepath="FO_dataset/truck/point_cloud_folder/reconstruction_30000.ply")

# Add samples to the dataset
dataset.add_sample(sample1)
dataset.add_sample(sample2)
dataset.add_sample(sample3)
dataset.add_sample(sample4)
dataset.add_sample(sample5)
dataset.add_sample(sample6)
dataset.add_sample(sample7)
dataset.add_sample(sample8)

# Launch FiftyOne App
session = fo.launch_app(dataset, auto=False, port=5152)
```

---

# **Visualization Results**

Below are sample screenshots showcasing the **3D Gaussian Splats reconstructions**:
![Image](https://github.com/user-attachments/assets/7b829255-b61b-4db8-b21c-dcc73796100a)

## **Drjohnson Scene**

![Image](https://github.com/user-attachments/assets/b580e602-9619-4f59-bc6d-95aa6cdeecd7)

## **Playroom Scene**

![Image](https://github.com/user-attachments/assets/989e3e2a-5be5-4ba0-bea6-2db3d2c16fad)
https://github.com/user-attachments/assets/1c3d3b6b-2b7b-4e93-8f5c-76a184f51260

## **Train Scene**

![Image](https://github.com/user-attachments/assets/a50f013d-e263-4e45-82c9-40f1f46ed24f)
https://github.com/user-attachments/assets/78ca63f5-1df9-4970-a50c-bfab0ee3615f

## **Truck Scene**

![Image](https://github.com/user-attachments/assets/89094b67-85da-4b16-814b-0dc65270ff5a)

---

# **Research & Applications**

This dataset is useful for a variety of **3D vision and AI applications**, including:

- **NeRF & Gaussian Splatting Benchmarking**
- **3D Scene Understanding & Reconstruction**
- **Multi-Modal AI (Images + 3D Point Clouds)**
- **Real-Time 3D Rendering Research**

---

# **Citation**

If you use this dataset, please cite the original **3D Gaussian Splatting** paper:

```bibtex
@article{kerbl2023gsplatting,
  title={3D Gaussian Splatting for Real-Time Radiance Field Rendering},
  author={Kerbl, Bernhard and Kopanas, Georgios and Leimkühler, Thomas and Drettakis, George},
  journal={arXiv preprint arXiv:2308.04079},
  year={2023}
}
```

# And also the link of this dataset in hugging Face: https://huggingface.co/datasets/pjramg/gaussian_splatting/