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https://github.com/limacv/GaussianSplattingViewer
Tiny Gaussian Splatting Viewer
https://github.com/limacv/GaussianSplattingViewer
gaussian-splatting pyopengl rendering
Last synced: 2 months ago
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Tiny Gaussian Splatting Viewer
- Host: GitHub
- URL: https://github.com/limacv/GaussianSplattingViewer
- Owner: limacv
- License: mit
- Created: 2023-10-06T04:36:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-15T06:53:04.000Z (10 months ago)
- Last Synced: 2024-05-19T08:09:16.660Z (8 months ago)
- Topics: gaussian-splatting, pyopengl, rendering
- Language: Python
- Homepage:
- Size: 1.06 MB
- Stars: 233
- Watchers: 5
- Forks: 22
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-3D-gaussian-splatting - OpenGL Viewer
README
# Tiny Gaussian Splatting Viewer
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.
# 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