用于图形硬件的延迟流

Timo Aila, Ville Miettinen, P. Nordlund
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引用次数: 80

摘要

在因果过程中,决策不依赖于未来的数据。由于未来的数据可能会改变当前事件的解释,许多众所周知的问题,如遮挡剔除、与顺序无关的透明度和边缘抗锯齿,不能使用传统的因果渲染架构来妥善解决。我们建议在顶点和像素处理单元之间增加一个延迟流。当一个三角形驻留在延迟流中时,随后的三角形生成遮挡信息。因此,三角形可能会被在它之后提交的原语剔除。在常见的基准测试场景中,我们显示像素处理和视频内存带宽使用的效率提高了2到4倍。我们还演示了如何通过使用延迟流来大幅降低与顺序无关的透明性的内存需求。最后,我们描述了如何在硬件中检测不连续边缘。因此,在自适应超采样中,先前用于折叠样本的启发式方法被连接信息所取代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Delay streams for graphics hardware
In causal processes decisions do not depend on future data. Many well-known problems, such as occlusion culling, order-independent transparency and edge antialiasing cannot be properly solved using the traditional causal rendering architectures, because future data may change the interpretation of current events.We propose adding a delay stream between the vertex and pixel processing units. While a triangle resides in the delay stream, subsequent triangles generate occlusion information. As a result, the triangle may be culled by primitives that were submitted after it. We show two-to fourfold efficiency improvements in pixel processing and video memory bandwidth usage in common benchmark scenes. We also demonstrate how the memory requirements of order-independent transparency can be substantially reduced by using delay streams. Finally, we describe how discontinuity edges can be detected in hardware. Previously used heuristics for collapsing samples in adaptive supersampling are thus replaced by connectivity information.
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