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arXiv · 2608.15785

RoofGS: Roofline-Guided End-to-End Acceleration of 3D Gaussian Splatting

Yang Luo·Yan Gong·Yongsheng Gao·Jie Zhao
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Abstract

3D Gaussian Splatting (3DGS) enables real-time novel-view synthesis but remains limited on GPUs at high resolutions. Through a stage-wise Roofline characterization, we identify two distinct hardware bottlenecks: global memory traffic dominates the front end, whereas instruction throughput limits rasterization. Guided by this analysis, we develop RoofGS, a rendering framework that applies bottleneck-specific optimizations rather than generic kernel acceleration. For the memory-bound front end, we design a resolution-adaptive quantized depth sorting key that compresses each key to 32 bits. For the compute-bound rasterizer, we introduce a range-aware bit-level fast exponential approximation tailored to the bounded exponent range after opacity culling, with a derived per-pixel error bound. These two core techniques are complemented by additional optimizations (kernel fusion, compact attribute storage, culling, dual-pixel evaluation) that additionally reduce memory traffic and improve instruction-level parallelism. Experiments show that RoofGS achieves a 10.1$\times$ end-to-end speedup over 3DGS at 4K on an RTX 4090, increasing throughput from 61 to 616 FPS, with only a 0.028 dB PSNR loss.

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Yang Luo, Yan Gong, Yongsheng Gao, Jie Zhao. 2026-08-16. RoofGS: Roofline-Guided End-to-End Acceleration of 3D Gaussian Splatting. https://arxiv.org/abs/2608.15785

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