---
pipeline_tag: image-to-3d     # Specify the task
library_name: fastgs       # Specify the library
language:
  - en                           # List languages your model supports
license: mit
base_model: fastgs/base 
big_model: fastgs/Big  
tags:                            # Add extra tags which would make the repo searchable using the tag
  - trianing acceleration
  - 3DGS
  - 3D Gaussian splatting
  - Novel view synthesis
---

<div align="center">
<h1>FastGS: Training 3D Gaussian Splatting in 100 Seconds</h1> 

[🌐 Homepage](https://fastgs.github.io/) | [📄 Paper](https://arxiv.org/abs/2511.04283) | [🖥️ GitHub](https://github.com/fastgs/FastGS)


</div>

<p align="center">
    <img src="assets/teaser_fastgs.jpg" width="800px"/>
</p>

## 🚀 What Makes FastGS Special?

FastGS is a **general acceleration framework** that supercharges 3D Gaussian Splatting training while maintaining Comparable rendering quality. Our method stands out with:

- **⚡ Blazing Fast Training**: Achieve SOTA results within **100 seconds**. **3.32× faster** than DashGaussian on Mip-NeRF 360 dataset. **15.45× acceleration** vs vanilla 3DGS on Deep Blending.
- **⚡ High fidelity**: Comparable rendering quality with SOTA methods
- **🎯 Easy Integration**: Seamlessly integrates with various backbones (Vanilla 3DGS, Scaffold-GS, Mip-splatting, etc.)
- **🛠️ Multi-Task Ready**: Proven effective across dynamic scenes, surface reconstruction, sparse-view, large-scale, and SLAM tasks
- **💡 Memory-Efficient**: Low GPU Memory requirements make it accessible for various hardware setups
- **🔧 Easy Deployment**: Simple post-training tool for feedforward 3DGS that works out-of-the-box