针对3D高斯点云渲染瓶颈，提出分阶段优化方案，实现4K下10倍加速。
RoofGS: Roofline-Guided End-to-End Acceleration of 3D Gaussian Splatting
- 基于屋顶模型分析定位内存与计算瓶颈，分别设计针对性优化。
- 4K分辨率下速度提升10.1倍，帧率从61增至616FPS，画质损失极小。
- 适合需要高分辨率实时渲染的图形应用，如元宇宙、VR内容生成。
3D高斯点云（3DGS）实现了实时新视角合成，但在高分辨率下仍受限于GPU性能。通过分阶段的屋顶模型分析，我们识别出两个关键硬件瓶颈：前端受全局内存流量主导，而光栅化阶段受限于指令吞吐量。基于此分析，我们提出RoofGS渲染框架，采用瓶颈特定优化而非通用内核加速。针对内存瓶颈，设计分辨率自适应量化深度排序键，将每个键压缩至32位；针对计算瓶颈，引入范围感知位级快速指数近似，适配透明度裁剪后的有限指数范围，并推导出每像素误差上限。两项核心技术辅以内核融合、紧凑属性存储、裁剪和双像素评估等优化，进一步降低内存流量并提升指令级并行性。实验表明，RoofGS在RTX 4090上4K分辨率下相比3DGS实现10.1倍端到端加速，帧率从61提升至616 FPS，仅造成0.028 dB PSNR损失。
原文摘要 · Abstract (English)
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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