PS-GS: Group-Wise Parallel Rendering with Stage-Wise Complexity Reductions for Real-Time 3D Gaussian Splatting
Joongho Jo, Jongsun Park · 2025
3D Gaussian Splatting (3D-GS) is an emerging rendering technique that surpasses the neural radiance field (NeRF) in both rendering speed and image quality. Despite its advantages, running 3D-GS on mobile or edge devices in real-time remains challenging due to large computational complexity. In this paper, we introduce PS-GS, a specialized low-complexity hardware designed to enhance the pipeline parallelism of 3D-GS rendering pipeline process. In this work, we first observe that 3D-GS rendering can be parallelized when the approximate order of Gaussians, from those closest to the camera to those farthest, is known ahead. But, to enhance 3D-GS rendering speed via parallel processing, an efficient viewpoint-adaptive grouping method with low computational costs is essential. Two key computational bottlenecks of viewpoint-adaptive grouping are the grouping of invisible Gaussians and depth-based sorting. For efficient group-wise parallel rendering with low complexity viewpoint-adaptive grouping, we propose three key techniques—cluster-based preprocessing, sorting, and grouping—all seamlessly incorporated into the PS-GS architecture. Our experimental results demonstrate that PS-GS delivers an average speedup of 1.20x with negligible peak signal-to-noise ratio (PSNR) degradation.