# FlashGS

**Repository Path**: huanghone/FlashGS

## Basic Information

- **Project Name**: FlashGS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-10-11
- **Last Updated**: 2025-10-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FlashGS
FlashGS is an efficient CUDA Python library, enabling real-time 3D Gaussian Splatting (3DGS) based rendering especially for large-scale and high-resolution (4K or even higher) scenes.

### [[Project Page](https://maxwellf1.github.io/flashgs_page)]

## Hardware Requirement
NVIDIA's server-grade and consumer-grade GPUs should work for our implementation. We have conducted our experiments on an NVIDIA A100, V100, RTX 2080ti, RTX 3090 and RTX 4090 GPUs.

## Directories
* `csrc/`: Our CUDA C++ implementation of FlashGS. The optimized rendering kernels are under `csrc/cuda_rasterizer/`.
* `example.py`: An example to show how to use the installed FlashGS library.
* `setup.py`: A Python script to build, package, and install the FlashGS library. 
* `requirements.txt`: Record some software dependencies when installing FlashGS.

## Installation
You can follow the following steps to setup on your machine:
* Clone the FlashGS project from this page.
* Download the dependencies as we recommend.
* Use `python setup.py install` or `pip install .` to install FlashGS library.
* Run `pip uninstall flash-gaussian-splatting` before you compile and install the new version.

## Run Example
* Download the pre-trained models. `https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip`
* Run `python example.py model_path`.
* Open `model_path/test_out` and check the result.