SwiftGS: Algorithm and System Co-Optimization for Fast 3D Gaussian Splatting on GPUs

Recent advances in 3D Gaussian Splatting (3DGS) have enabled high-quality and efficient novel view synthesis, demonstrating great potential in real-world applications such as robotic perception and digital-twin construction. However, 3DGS requires processing up to millions of Gaussians in parallel, imposing significant computational and memory demands that limit its deployment on resource-constrained platforms. Through systematic profiling and analysis, this paper identifies several redundancy at both the algorithmic and system implementation levels. These insights motivate us to explore several novel optimizations, including adaptive early sorting, GPU-efficient axis-shared rasterization, and dynamic thresholding. Unlike prior work that focuses only on either algorithmic improvements or systems optimization, our approach explores a joint algorithm and system co-optimization to push the performance limits of 3DGS on GPUs. Comprehensive evaluation demonstrates that our co-optimization approach, named \textit{Flash3DGS} achieves a speed-up of up to $1.41 \times$ with negligible algorithmic performance drop in rendering image quality compared with the \textit{gsplat} baseline. Importantly, our co-optimization is orthogonal to most existing 3DGS acceleration methods, allowing for synergistic performance gains when used in combination. We plan to release our code publicly upon paper acceptance to support reproducibility and future research.