提出新型纹理过滤算法，实现零误差且仅需一次采样即可完成放大滤波。
Collaborative Texture Filtering
- 利用波通信在着色器内共享解码像素值，避免重复采样。
- 放大倍数足够大时，每像素仅需≤1次采样即可实现零误差滤波。
- 适用于需要高质量纹理放大的实时渲染场景，如游戏与图形应用。
近期纹理压缩技术大幅提升了压缩比，但无法利用GPU的纹理单元进行解压和滤波。为此发展出随机纹理滤波（STF）以避免多次像素采样的高成本。然而，这些方法在放大时会出现不理想的视觉变化，即使使用时空去噪器仍可能产生可见噪声和闪烁。最新工作通过在邻近像素间共享解码像素值，显著改善了放大滤波质量（Wronski 2025）。借助GPU波通信原语，该共享可在活跃着色器中无内存开销地完成。本文进一步提出新算法，利用波通信在通道间分发任务，避免滤波前的重复解压。通过合理分配各通道的工作量，当放大倍数足够大时，可实现每像素≤1次采样即达零误差滤波。对于剩余情况，提出新的降级滤波方法，其质量也优于以往方案。
原文摘要 · Abstract (English)
Recent advances in texture compression provide major improvements in compression ratios, but cannot use the GPU's texture units for decompression and filtering. This has led to the development of stochastic texture filtering (STF) techniques to avoid the high cost of multiple texel evaluations with such formats. Unfortunately, those methods can give undesirable visual appearance changes under magnification and may contain visible noise and flicker despite the use of spatiotemporal denoisers. Recent work substantially improves the quality of magnification filtering with STF by sharing decoded texel values between nearby pixels (Wronski 2025). Using GPU wave communication intrinsics, this sharing can be performed inside actively executing shaders without memory traffic overhead. We take this idea further and present novel algorithms that use wave communication between lanes to avoid repeated texel decompression prior to filtering. By distributing unique work across lanes, we can achieve zero-error filtering using <=1 texel evaluations per pixel given a sufficiently large magnification factor. For the remaining cases, we propose novel filtering fallback methods that also achieve higher quality than prior approaches.
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