This work introduces FlashGS, an open-source CUDA Python library, designed to facilitate the efficient differentiable rasterization of 3D Gaussian Splatting through algorithmic and kernel-level optimizations. FlashGS is developed based on the observations from a comprehensive analysis of the rendering process to enhance computational efficiency and bring the technique to wide adoption. The paper includes a suite of optimization strategies, encompassing redundancy elimination, efficient pipelining, refined control and scheduling mechanisms, and memory access optimizations, all of which are meticulously integrated to amplify the performance of the rasterization process. An extensive evaluation of FlashGS' performance has been conducted across a diverse spectrum of synthetic and real-world large-scale scenes, encompassing a variety of image resolutions. The empirical findings demonstrate that FlashGS consistently achieves an average 4x acceleration over mobile consumer GPUs, coupled with reduced memory consumption. These results underscore the superior performance and resource optimization capabilities of FlashGS, positioning it as a formidable tool in the domain of 3D rendering.
本研究介绍了FlashGS，一款开源的CUDA Python库，旨在通过算法和内核级优化，促进3D高斯点绘的高效可微光栅化。FlashGS的开发基于对渲染过程的全面分析，旨在提高计算效率并推动这一技术的广泛应用。本文详细描述了一系列优化策略，包括冗余消除、有效的流水线处理、精细化的控制与调度机制，以及内存访问优化，这些策略经过精心整合，极大地提升了光栅化过程的性能。 我们对FlashGS的性能进行了广泛评估，涵盖了多种分辨率的合成和现实世界的大规模场景。实证结果显示，FlashGS在移动消费级GPU上实现了平均4倍的加速，并且显著减少了内存消耗。这些结果凸显了FlashGS在性能提升和资源优化方面的卓越能力，使其成为3D渲染领域中一款强大的工具。