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TL;DR: An efficient and research-friendly Gaussian Splatting framework

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Florian Hahlbohm, Linus Franke, Martin Eisemann, Marcus Magnor

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Description

Project page for Faster-GS, a CVPR 2026 Gaussian Splatting optimization codebase by Florian Hahlbohm, Linus Franke, Martin Eisemann, and Marcus Magnor. This URL is the research site for the framework.

It consolidates strong 3DGS training strategies, adds further optimizations, and studies numerical stability, Gaussian truncation, and gradient approximation. Results report up to about 5x faster training at matched quality, plus 4D non-rigid extensions. Options include a native NeRFICG PyTorch plus CUDA build and a CUDA backend for pipelines such as Inria 3DGS.

Aimed at advanced researchers and engineers who already train or extend 3DGS. NeRF-style optimization and CUDA/PyTorch familiarity is assumed.

Free GitHub-hosted page with abstract, BibTeX for the arXiv preprint, and links to both integration paths.

Useful as a cost-aware baseline when comparing 3DGS training speedups fairly.