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首页> 外文期刊>ACM Transactions on Graphics >Delay Streams for Graphics Hardware
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Delay Streams for Graphics Hardware

机译:图形硬件的延迟流

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摘要

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.
机译:在因果过程中,决策不依赖于将来的数据。使用传统的因果渲染体系结构无法正确解决许多众所周知的问题,例如遮挡剔除,与顺序无关的透明度和边缘抗锯齿,因为将来的数据可能会改变当前事件的解释。我们建议在顶点和像素处理单元之间添加延迟流。当三角形位于延迟流中时,后续的三角形会生成遮挡信息。结果,三角形可能被其后提交的图元剔除。在常见的基准场景中,我们在像素处理和视频内存带宽使用方面显示了2到4倍的效率提升。我们还演示了如何通过使用延迟流显着降低顺序无关的透明性的内存要求。最后,我们描述如何在硬件中检测不连续边缘。因此,在连接性信息中取代了以前在自适应超级采样中用于折叠样本的启发式算法。

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