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- FastGS: Training 3D Gaussian Splatting in 100 Seconds - GitHub
🚀 What Makes FastGS Special? FastGS is a general acceleration framework that supercharges 3D Gaussian Splatting training while maintaining Comparable rendering quality
- FastGS
1 What is FastGS? TL;DR: FastGS is a new, simple, and general acceleration framework for 3D Gaussian Splatting (3DGS) It enables training a scene in about 100 seconds while maintaining comparable rendering quality
- FastGS: Training 3D Gaussian Splatting in 100 Seconds
In this paper, we propose FastGS, a novel, simple, and general acceleration framework that fully considers the importance of each Gaussian based on multi-view consistency, efficiently solving the trade-off between training time and rendering quality
- fastgs FastGS | DeepWiki
What is FastGS? FastGS is a general acceleration framework for 3D Gaussian Splatting (3DGS) that achieves 100-second training times while maintaining rendering quality comparable to state-of-the-art methods
- FastGS: An acceleration framework for 3D Gaussian Splatting . . .
FastGS is a general acceleration framework designed to speed up 3D Gaussian Splatting training It achieves state-of-the-art results in seconds, offering substantial speed improvements over existing methods
- FastGS README. md at main - GitHub
🚀 What Makes FastGS Special? FastGS is a general acceleration framework that supercharges 3D Gaussian Splatting training while maintaining Comparable rendering quality
- FastGS: Training 3D Gaussian Splatting in 100 Seconds
## 🚀 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
- GitHub - Jonnnty VLM-Instruct-FastGS: VLM-Instruct-FastGS enhances 3D . . .
This project is built upon 3DGS, FastGS, and Qwen3-VL-2B-Instruct We extend our gratitude to all the authors for their outstanding contributions and excellent repositories!
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