通过共享采样值提升纹理放大质量，显著降低伪影。
Improved Stochastic Texture Filtering Through Sample Reuse
- 利用相邻像素间相似性共享纹理采样值，成本仅微增0.04-0.14毫秒/帧
- 高倍放大下相比单样本STF提升超10 dB PSNR，图像差异明显减小
- 适用于需要高质量纹理放大的实时渲染场景，尤其适合神经纹理压缩
随机纹理过滤（STF）重新成为降低先进纹理压缩方法（如神经纹理压缩）中纹理过滤成本的有效技术。然而，在纹理放大过程中，由于过滤与着色顺序交换，可能导致混叠现象；且无法对存储于纹理中的材质属性（如表面法线）进行平滑插值，从而引发外观异常。本文提出一种新方法，改善随机过滤后放大纹理的质量，并减少与传统纹理过滤的图像差异。当纹理被放大时，邻近像素会过滤相似的纹理元素，我们引入了在几乎不增加成本的前提下（每帧仅增加0.04–0.14毫秒），实现纹理值共享的技术。同时改进加权重要性采样，确保本方法的误差不会超过单样本随机纹理过滤。在高倍放大条件下，该方法的PSNR优于单样本STF超过10 dB。实验结果表明，无论是否使用时空去噪，图像质量均有显著提升。
原文摘要 · Abstract (English)
Stochastic texture filtering (STF) has re-emerged as a technique that can bring down the cost of texture filtering of advanced texture compression methods, e.g., neural texture compression. However, during texture magnification, the swapped order of filtering and shading with STF can result in aliasing. The inability to smoothly interpolate material properties stored in textures, such as surface normals, leads to potentially undesirable appearance changes. We present a novel method to improve the quality of stochastically-filtered magnified textures and reduce the image difference compared to traditional texture filtering. When textures are magnified, nearby pixels filter similar sets of texels and we introduce techniques for sharing texel values among pixels with only a small increase in cost (0.04--0.14~ms per frame). We propose an improvement to weighted importance sampling that guarantees that our method never increases error beyond single-sample stochastic texture filtering. Under high magnification, our method has >10 dB higher PSNR than single-sample STF. Our results show greatly improved image quality both with and without spatiotemporal denoising.
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