FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering
This work addresses performance bottlenecks in 3D rendering for applications requiring efficient processing of complex scenes, though it is incremental as it builds on existing Gaussian Splatting methods.
The paper tackles the computational inefficiency of 3D Gaussian Splatting for large-scale, high-resolution rendering by introducing FlashGS, an optimized CUDA Python library that achieves an average 4x acceleration over mobile consumer GPUs with reduced memory consumption.
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.