CaT-GS：通过帧间缓存和瓦片调度实现大规模场景的高效3DGS渲染
CaT-GS: Efficient 3DGS Rendering for Large Scale Scenes via Inter-frame Caching and Tile Scheduling
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中文总结 AI 辅助
针对3DGS在大规模场景中性能下降问题，CaT-GS提出通过推测性多帧预处理、帧间缓存机制及重构光栅化任务来消除冗余与减轻负载不平衡，实验证明其显著提升渲染速度，为大规模场景高保真实时渲染建立新基准。
中文摘要 AI 辅助
近期3D高斯渲染（3DGS）的突破推动了神经渲染的发展，但其在大规模场景中性能因基于瓦片的光栅化计算负担而显著下降。现有优化要么需昂贵的场景重新训练，要么关注管道的狭窄方面。通过分析，发现冗余的帧间预处理、基于视点的遮挡冗余和严重的瓦片级负载不平衡。为此提出CaT-GS，引入推测性多帧预处理和帧间缓存机制消除冗余，用专用内核重构光栅化任务减轻负载不平衡，实验表明其比原3DGS加速达10倍，比先前方法高70%，为大规模场景高保真实时渲染建立了新基准。
英文摘要
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.
发表机构
- Shanghai Jiao Tong University（上海交通大学）
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
