[論文レビュー] Faster-GS: Analyzing and Improving Gaussian Splatting Optimization

Florian Hahlbohm, Linus Franke+2 more|arXiv (Cornell University)|Feb 10, 2026

Computer Graphics and Visualization Techniques被引用数 0

ひとこと要約

tldr: Faster-GS は効果的な 3D Gaussian Splatting 学習最適化を統合・拡張し、再構成品質を保ちつつ最大で 5× の学習速度を達成し、4D（ダイナミック）シーンへの利得を拡張します。

ABSTRACT

Recent advances in 3D Gaussian Splatting (3DGS) have focused on accelerating optimization while preserving reconstruction quality. However, many proposed methods entangle implementation-level improvements with fundamental algorithmic modifications or trade performance for fidelity, leading to a fragmented research landscape that complicates fair comparison. In this work, we consolidate and evaluate the most effective and broadly applicable strategies from prior 3DGS research and augment them with several novel optimizations. We further investigate underexplored aspects of the framework, including numerical stability, Gaussian truncation, and gradient approximation. The resulting system, Faster-GS, provides a rigorously optimized algorithm that we evaluate across a comprehensive suite of benchmarks. Our experiments demonstrate that Faster-GS achieves up to 5$ imes$ faster training while maintaining visual quality, establishing a new cost-effective and resource efficient baseline for 3DGS optimization. Furthermore, we demonstrate that optimizations can be applied to 4D Gaussian reconstruction, leading to efficient non-rigid scene optimization.

研究の動機と目的

• Survey and consolidate performance-enhancing strategies in 3D Gaussian Splatting (3DGS) prior work.
• Identify bottlenecks in training and propose broadly applicable optimizations.
• Integrate novel optimizations to form a fast, modular 3DGS training pipeline (Faster-GS).
• Demonstrate that optimizations generalize to multi-dimensional (4D) Gaussian reconstruction.

提案手法

• Review and categorize memory-access bottlenecks in 3DGS training.
• Integrate techniques: tighter, opacity-aware 2D bounding boxes; per-tile splat lists; two-stage sorting with stable sort; per-Gaussian backward pass; fused activations; memory-coalesced layouts; fused Adam updates; z-ordering of Gaussians during densification; fusion of backward pass with optimizer steps.
• Apply all optimizations to the original CUDA-based differentiable rasterization pipeline while preserving compatibility with common 3DGS codebases.
• Extend the optimized pipeline to 4D Gaussian Splatting for dynamic scene reconstruction.
• Provide a refactored basis implementation to improve numerical stability and VRAM handling; enable easy integration into existing 3DGS frameworks.

実験結果

リサーチクエスチョン

• RQ1 How much faster can training become when applying a unified set of memory- and computation-centric optimizations to 3DGS without compromising reconstruction quality?
• RQ2 Which optimizations yield the largest speedups and memory savings when evaluated across standard 3DGS benchmarks and datasets?
• RQ3 Can the optimization techniques for 3DGS training be extended to 4D (dynamic) Gaussian Splatting without loss of fidelity or performance?
• RQ4 What is the effect of fused updates and per-Gaussian backward passes on training stability and VRAM usage?

主な発見

• Faster-GS は元の 3DGS 実装と比較して benchmark シーンで最大 5× の学習速度を達成しつつ再構成品質を維持します。
• 3DGS ベンチマークの全品質および全 Gaussian を用いた平均再構成時間は Faster-GS パイプライン下で 163 秒です。
• さまざまな最適化によりメモリトラフィックを低減しキャッシュ局所性を改善し、複数のデータセット（Mip-NeRF360、Tanks and Temples、Deep Blending）で大幅な速度向上を実現します。
• 洗練された基盤実装は追加の最適化適用前でもすでに顕著な利益をもたらし（ベースラインより約 15% 快適）、その後の最適化でさらに改善します。
• 勾配計算とパラメータ更新の融合、各 Gaussian に対する後向伝播、および活性化カーネルの融合により、学習時間と VRAM 使用量の顕著な削減を達成します（屋外/屋内シーンを横断するアブレーション例）。
• このフレームワークは 4D Gaussian Reconstruction に拡張され、非剛性ダイナミックシーン最適化を効率化します。