https://github.com/jaesung-cs/vkgs
Vulkan-based Gaussian Splatting viewer, and python binding
https://github.com/jaesung-cs/vkgs
Last synced: about 1 month ago
JSON representation
Vulkan-based Gaussian Splatting viewer, and python binding
- Host: GitHub
- URL: https://github.com/jaesung-cs/vkgs
- Owner: jaesung-cs
- License: mit
- Created: 2024-03-17T01:37:33.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-17T13:31:27.000Z (3 months ago)
- Last Synced: 2025-03-17T14:40:07.998Z (3 months ago)
- Language: C++
- Homepage:
- Size: 4.45 MB
- Stars: 83
- Watchers: 2
- Forks: 10
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-3D-gaussian-splatting - vkgs - cross-platform, high performance 3DGS renderer in C++ and Vulkan Compute/Graphics (Open Source Implementations / Viewers)
README
# vkgs
Gaussian splatting viewer written in Vulkan.
The main goal of this project is to maximize rendering speed.
For more details, refer to [details](DETAILS.md).
## Notes
Currently I am **not actively maintaining** this project, but I am still open to suggestions or discussions, and will be happy to answer any questions or make quick fixes. Please open github issues about anything. Job offer is also welcome ;)
Consider using [vk_gaussian_splatting](https://github.com/nvpro-samples/vk_gaussian_splatting), another Vulkan 3DGS renderer by NVidia with richer functionalities.
They mentioned my project (which is a great honor for me), and obviously their codes are far more well-organized.
Also, check out [vulkan_radix_sort](https://github.com/jaesung-cs/vulkan_radix_sort).
It has a way better user-friendly interface that can be easily used by other projects.
It is adopted by the NVidia sample, implying that the sort algorithm is quite stable.
## Desktop Viewer
Viewer works with pre-trained vanilla 3DGS models as input.
### Feature Highlights
- Fast rendering speed
- 350+ FPS on 1600x900, high-end GPU (NVidia GeForce RTX 4090)
- 50+ FPS on 1600x900, high-end MacOS laptop (Apple M2 Pro)
- 1-1.5x speed compared to SIBR viewer, but difference becomes bigger when scene is zoomed out,
- because the number of tiles increases, and
- more splats overlap in a single tile, so sequential blending operation takes more time
- Using graphics pipeline
- Draw gaussian splats over other opaque objects, interacting with depth buffer
- 100% GPU tasks
- No CPU-GPU synchronization for single frame: while GPU is working on frame i, CPU prepares a commands buffer and submits for frame i+1. No synchronization for frame i to get number of visible splats.
- Indirect sort & draw: sorting and rendering only visible points
- My vulkan radix sort implementation
### Requirements
- `VulkanSDK>=1.2`
- Download the latest version from https://vulkan.lunarg.com/ and follow install instruction.
- `cmake>=3.15`
### Dependencies
- submodules
```bash
$ git submodule update --init --recursive
```
- VulkanMemoryAllocator
- glm
- glfw
- imgui
- argparse
- [vulkan_radix_sort](https://github.com/jaesung-cs/vulkan_radix_sort): my Vulkan/GLSL implementation of reduce-then-scan radix sort.
### Build
```bash
$ cmake . -B build
$ cmake --build build --config Release -j
```
### Run
```bash
$ ./build/vkgs_viewer # or ./build/Release/vkgs_viewer
$ ./build/vkgs_viewer -i
```
Drag and drop pretrained .ply file from [official gaussian splatting](https://github.com/graphdeco-inria/gaussian-splatting), Pre-trained Models (14 GB).
- Left drag to rotate.
- Right drag to translate.
- Left+Right drag to zoom in/out.
- WASD, Space to move.
- Wheel to zoom in/out.
- Ctrl+wheel to change FOV.
## pygs: Python Binding (WIP)
GUI is created in an off thread.
According to GLFW documentation, the user should create window in main thread.
However, managing windows off-thread seems working in Windows and Linux somehow.
Unfortunately, MacOS doesn't allow this.
MacOS’s UI frameworks can only be called from the main thread.
Here's a related [thread](https://forums.developer.apple.com/forums/thread/659010) by Apple staff.
### Requirements
- Windows or Linux (Doesn't work for MacOS.)
- conda: cmake, pybind11, cuda-toolkit (cuda WIP, not necessary yet)
```bash
$ conda create -n pygs python=3.10
$ conda activate pygs
$ conda install conda-forge::cmake
$ conda install conda-forge::pybind11
$ conda install nvidia/label/cuda-12.2.2::cuda-toolkit # or any other version
```
### Build
The python package dynamically links to c++ shared library file.
So, first build the shared library first, then install python package.
```bash
$ cmake . -B build
$ cmake --build build --config Release -j
$ pip install -e binding/python
```
### Test
```bash
$ python
>>> import pygs
>>> pygs.show()
>>> pygs.load("./models/bicycle_30000.ply") # asynchronously load model to viewer
>>> pygs.load("./models/garden_30000.ply")
>>> pygs.close()
```