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Stochastic Transparency

机译:随机透明度

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摘要

Stochastic transparency provides a unified approach to order-independent transparency, antialiasing, and deep shadow maps. It augments screen-door transparency using a random sub-pixel stipple pattern, where each fragment of transparent geometry covers a random subset of pixel samples of size proportional to alpha. This results in correct alpha-blended colors on average, in a single render pass with fixed memory size and no sorting, but introduces noise. We reduce this noise by an alpha correction pass, and by an accumulation pass that uses a stochastic shadow map from the camera. At the pixel level, the algorithm does not branch and contains no read-modify-write loops, other than traditional z-buffer blend operations. This makes it an excellent match for modern massively parallel GPU hardware. Stochastic transparency is very simple to implement and supports all types of transparent geometry, able without coding for special cases to mix hair, smoke, foliage, windows, and transparent cloth in a single scene.
机译:随机透明度提供了一种与顺序无关的透明度,抗锯齿和深阴影贴图的统一方法。它使用随机的子像素点画图案来增强屏幕门的透明度,其中透明几何图形的每个片段都覆盖大小与alpha成比例的像素样本的随机子集。这样,在具有固定内存大小且不进行排序的单个渲染过程中,平均可以得到正确的alpha混合颜色,但是会引入噪点。我们通过alpha校正遍历和通过使用来自相机的随机阴影图的累积遍历来减少这种噪声。在像素级别,除传统的z缓冲区混合操作外,该算法不分支并且不包含任何读取-修改-写入循环。这使其非常适合现代大规模并行GPU硬件。随机透明性非常易于实现,并且支持所有类型的透明几何体,无需进行特殊编码即可在单个场景中混合头发,烟雾,树叶,窗户和透明布料。

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