CaT-GS：基于帧间缓存与瓦片调度的大规模场景高效3DGS渲染
CaT-GS: Efficient 3DGS Rendering for Large-Scale Scenes with Inter-frame Caching and Tile Scheduling
针对3DGS大规模场景渲染中的冗余计算与瓦片负载不均衡问题，提出帧间缓存与专用光栅化内核，可直接用于加速现有3DGS管线而无需重训练。
原文依据 CaT-GS achieves a speedup of up to 10 times over the original 3DGS
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期三维高斯泼溅（3DGS）的突破以高保真度和速度推进了神经渲染。然而，由于基于瓦片的光栅化带来的计算负担，其在大规模场景中的性能显著下降。现有优化工作要么需要昂贵的场景重训练，要么只关注管线中的狭窄方面，忽视了实际部署中的关键低效问题。通过全面分析，我们识别出冗余和低GPU利用率的三个主要来源：冗余的帧间预处理、基于视角的遮挡冗余以及严重的瓦片级负载不均衡。为解决这些问题，我们提出CaT-GS，一种新颖且高效的3DGS渲染管线。CaT-GS引入推测性多帧预处理方法以消除连续帧间的冗余计算，并采用帧间缓存机制以消除视角冗余的渲染阶段。此外，它通过专用内核重构光栅化任务以缓解瓦片负载不均衡，显著提升GPU利用率。大量实验表明，CaT-GS相比原始3DGS实现高达10倍的加速，相比先前最优方法提升高达70%，为大规模场景的高保真实时渲染建立了新的基准。
英文原文摘要 1343 字符
Recent breakthroughs in 3D Gaussian Splatting (3DGS) have advanced neural rendering with high fidelity and speed. However, its performance degrades significantly in large-scale scenes due to the computational burden of tile-based rasterization. Existing optimization efforts either require costly scene re-training or focus on narrow aspects of the pipeline, overlooking critical inefficiencies in real-world deployments. Through a comprehensive analysis, we identify three primary sources of redundancy and low GPU utilization: redundant inter-frame pre-processing, viewpoint-based occlusion redundancy, and severe tile-level load imbalance. To address these issues, we propose CaT-GS, a novel and efficient 3DGS rendering pipeline. CaT-GS introduces a speculative multi-frame preprocessing method to eliminate redundant computations across consecutive frames, and an inter-frame caching mechanism to eliminate viewpoint redundant rendering stages. Furthermore, it refactors rasterization tasks with a dedicated kernel to mitigate tile load imbalance, significantly boosting GPU utilization. Extensive experiments demonstrate that CaT-GS achieves a speedup of up to 10 times over the original 3DGS and up to 70% over previous state-of-the-art methods, establishing a new benchmark for high-fidelity, real-time rendering of large-scale scenes.