发表机构
Arizona State University; Johns Hopkins University（亚利桑那州立大学; 约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TileSkipper通过静态逐高斯截断策略实现区域自适应瓦片剪枝，在13个场景上获得最高1.238倍加速且PSNR损失极小，无需额外推理开销。
AI 中文摘要
分块3D高斯泼溅光栅化器在瓦片枚举时通常使用单一的场景级贡献截断阈值，尽管不同内容对支持截断的敏感度存在差异。TileSkipper为冻结检查点选择静态的逐高斯截断策略。校准渲染度量候选对的节省量，以及一个考虑前向透射率和背景颜色的隔离移除失真代理。该方法在64个高斯组间分配截断阈值，并且仅在不相交的选择视图上完成完整渲染后才接受策略。导出的策略每个高斯使用一个字节，无需参数更新、额外内核或逐帧策略推理。在来自Mip-NeRF 360、Tanks & Temples和Deep Blending的13个场景上，固定策略的AccuTile扫描在标准分辨率下给出数据集宏平均加速比1.088倍，在3840像素宽下为1.238倍，平均PSNR变化为-0.007/-0.023 dB。与现有不透明度感知边界的六种集成产生1.009倍至1.121倍的仅编译器加速。对于来自3σ光栅化器的四个移植，我们分别归因于先前精确边界转换和我们的增量增益。匹配质量的消融显示相对于场景级校准有适度增益，与逐高斯控制相当；与AdaGScale的独立比较取决于具体场景设置。
英文摘要
Tiled 3D Gaussian Splatting rasterizers often use one scene-wide contribution cutoff for tile enumeration, although content differs in its sensitivity to support truncation. TileSkipper selects a static per-Gaussian cutoff policy for a frozen checkpoint. Calibration renders measure candidate pair savings and an isolated-removal distortion proxy that accounts for front transmittance and background color. The method allocates cutoffs across 64 Gaussian groups and accepts policies only after complete renders on disjoint selection views. The exported policy uses one byte per Gaussian, with no parameter updates, additional kernel, or per-frame policy inference. Across 13 scenes from Mip-NeRF 360, Tanks & Temples, and Deep Blending, a fixed-policy AccuTile sweep gives dataset-macro speedups of $1.088\times$ at standard resolution and $1.238\times$ at 3840 pixels wide, with $-0.007/-0.023$ dB mean PSNR change. Six integrations with existing opacity-aware bounds yield $1.009\times$--$1.121\times$ compiler-only speedups. For four ports from $3σ$ rasterizers, we separately attribute the prior exact-bound transition and our incremental gain. Matched-quality ablations show modest gains over scene-global calibration and parity with per-Gaussian control; the standalone comparison with AdaGScale is regime-dependent.
