提出无遍历并行过滤与自适应压缩，提升大规模3D高斯点云渲染速度
FilterGS: Traversal-Free Parallel Filtering and Adaptive Shrinking for Large-Scale LoD 3D Gaussian Splatting
- 设计双滤器并行筛选高斯元素，避免串行遍历耗时超60%
- 引入新指标GTC量化冗余对，实现场景自适应压缩
- 在多大数据集上实现顶尖渲染速度，适合大场景实时渲染应用
3D高斯点云渲染实现了实时性能的突破。然而，使用层次细节（LoD）方法扩展至大场景时面临两大挑战：串行遍历消耗超过60%渲染时间，以及冗余的高斯-瓦片键值对带来额外处理开销。为此，我们提出FilterGS，采用两种互补的并行过滤机制，无需树遍历即可高效选择高斯元素。此外，我们提出一种新型GTC指标，用于量化高斯-瓦片键值对的冗余性，并基于此设计场景自适应的高斯压缩策略，有效减少冗余对。大量实验表明，FilterGS在多个大规模数据集上实现了业界领先的渲染速度，同时保持了良好的视觉质量。
原文摘要 · Abstract (English)
3D Gaussian Splatting has revolutionized neural rendering with real-time performance. However, scaling this approach to large scenes using Level-of-Detail methods faces critical challenges: inefficient serial traversal consuming over 60\% of rendering time, and redundant Gaussian-tile pairs that incur unnecessary processing overhead. To address these limitations, we introduce FilterGS, featuring a parallel filtering mechanism with two complementary filters that select Gaussian elements efficiently without tree traversal. Additionally, we propose a novel GTC metric that quantifies the redundancy of Gaussian-tile key-value pairs. Based on this metric, we introduce a scene-adaptive Gaussian shrinking strategy that effectively reduces redundant pairs. Extensive experiments demonstrate that FilterGS achieves state-of-the-art rendering speeds while maintaining competitive visual quality across multiple large-scale datasets. Project page: https://github.com/xenon-w/FilterGS
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