GSReuse: Temporally Adaptive Screen-Space Reuse for Accelerating 3D Gaussian Splatting

Recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time, high-fidelity novel view synthesis. However, rendering each frame independently in a video sequence leads to redundant computations, especially when adjacent frames share significant visual overlaps. This inefficiency is particularly problematic in VR applications, where high frame rates and stereoscopic rendering amplify the per-frame cost. Existing frame interpolation or reuse strategies typically rely on image-domain information and are thus not directly applicable to 3DGS rendering, which is fundamentally point-based. To address this runtime inefficiency, we propose GSReuse, a lightweight and drop-in accelerator that speeds up 3DGS rendering by reusing computations across consecutive frames. GSReuse operates in screen space and introduces only minimal modifications to existing 3DGS rendering pipelines. It also eliminates the need for retraining scene representations. Given the rendered image, depth map, and camera parameters of the current frame, GSReuse estimates reliable Gaussian splatting motion vectors for all pixels and warps reusable contents to the new view. A tile-based filtering and masking strategy is then applied to determine which regions can be safely reused, allowing the 3DGS renderer to skip redundant rendering operations. We evaluate GSReuse on multiple benchmark datasets, showing that GSReuse significantly improves rendering, while maintaining high visual fidelity. Compared to state-of-the-art video frame reuse/generation methods, GSReuse delivers better image quality with much lower latency, facilitating practical deployment of 3DGS in VR applications.