fastgs/FastGS

Health v2 · maintenance only

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Adoption not part of the score

1221 stars · 127 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

FastGS is a general acceleration framework for 3D Gaussian Splatting that trains scenes in roughly 100 seconds using multi-view consistent densification and targeted pruning. It integrates with multiple 3DGS backbones and supports tasks including dynamic scenes, surface reconstruction, sparse-view, large-scale reconstruction, and SLAM.

Use cases

• accelerate 3d gaussian splatting training
• reconstruct 3d scenes from photos quickly
• speed up novel view synthesis training
• train 3dgs on limited gpu memory
• sparse-view 3d reconstruction
• surface reconstruction from multi-view images
• speed up dynamic scene reconstruction

When to choose

• you need fast 3DGS training without sacrificing rendering quality
• you want to accelerate an existing 3DGS backbone like Mip-Splatting or Scaffold-GS
• you work on dynamic scenes, sparse-view, or SLAM tasks needing faster Gaussian splatting

When to avoid

• you need non-Gaussian-splatting rendering methods like NeRF or mesh-based pipelines
• you require a turnkey GUI application rather than a research codebase
• your project depends on a license other than MIT

Facets

library · maturity active

machine-learning graphics image-processing gpu-computing computer-vision graphics machine-learning simulation python cross-platform 3d-gaussian-splatting 3dgs neural-rendering scene-reconstruction training-acceleration cvpr-2026 novel-view-synthesis gpu linux

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markdown · JSON · MCP: product_card(name="fastgs/FastGS")