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https://github.com/limacv/GaussianSplattingViewer

Tiny Gaussian Splatting Viewer
https://github.com/limacv/GaussianSplattingViewer

gaussian-splatting pyopengl rendering

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Tiny Gaussian Splatting Viewer

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# Tiny Gaussian Splatting Viewer

![UI demo](assets/teaser.png)

This is a simple Gaussian Splatting Viewer built with PyOpenGL / CUDARasterizer. It's easy to install with minimum dependencies. The goal of this project is to provide a minimum example of the viewer for research and study purpose. 



# News!

1/10/2024: The OpenGL renderer has faster sorting backend with `torch.argsort` & `cupy.argsort`. With cuda based sorting, it achieves nearly real-time sorting with OpenGL backend.



12/21/2023: Now we support rendering using the official cuda rasterizer!



# Usage

Install the dependencies:

```

pip install -r requirements.txt

```



Launch the viewer:

```

python main.py

```



You can check how to use UI in the "help" panel.



The Gaussian file loader is compatiable with the official implementation. 

Therefore, download pretrained Gaussian PLY file from [this official link](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip), and select the "point_cloud.ply" you like by clicking the 'open ply' button, and you are all set!



# Optional dependencies:



- If you want to use `cuda` backend for rendering, please install the [diff-gaussian-rasterization](https://github.com/graphdeco-inria/diff-gaussian-rasterization) following the guidance [here](https://github.com/graphdeco-inria/gaussian-splatting). And also install the following package:

```

pip install cuda-python

```



- For sorting, we provide three backend: `torch`, `cupy`, and `cpu`. The implementation will choose the first available one based on this priority order: `torch -> cupy -> cpu`. If you have `torch` or `cupy` backend, turning on `auto sort` will achieve nearly real-time sorting.

    - If you want to use `torch` as sorting backend, install any version of [PyTorch](https://pytorch.org/get-started/locally/).



    - If you want to use `cupy` to accelerate sorting, you should install the following package:

    ```

    pip install cupy-cuda11x // for cuda 11

    pip install cupy-cuda12x // for cuda 12

    ```



# Troubleshoot



The rendering speed of is comparable to the official CUDA renderer. If you're experiencing slow rendering, it's likely you are using integrated graphics card instead of a high-performance one. You can configure python to use high-performance graphics card in system settings. In Windows, you can set in Setting > System > Display > Graphics. See the screenshot below for example.



![Setting > System > Display > Graphics](assets/setting.png)



# Limitations

- The implementation utilizes SSBO, which is only support by OpenGL version >= 4.3. Although this version is widely adopted, MacOS is an exception. As a result, this viewer does not support MacOS.



- The `cuda` backend currently does not support other visualizations.



- Based on the flip test between the two backends, the unofficial implementation seems producing slightly different results compared with the official cuda version.



# TODO

- Add orthogonal projection

- Make the projection matrix compatiable with official cuda implementation

- Tighter billboard to reduce number of fragments

- Save viewing parameters