Scalable Programmable Motion Effects on GPUs

Scalable Programmable Motion Effects on GPUs
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GPU 上的可扩展可编程运动效果

DOI:
10.1111/j.1467-8659.2012.03219.x
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发表时间:
2012-09
影响因子:
2.5
通讯作者:
任重
任重
中科院分区:
计算机科学4区
文献类型:
--
作者:
任重

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我们提出了一个有效的和可扩展的系统,使GPU上的可编程运动效果。我们的系统基于Schmid等人提出的框架。[SSBG 10]将表面着色器的概念扩展到可编程运动效果。虽然能够表达各种运动描绘风格,但运动效果程序的执行需要关于在任意长的时间段期间在像素前面经过的对象的表面的所有部分的全局知识,导致极高的存储器使用并且显著地限制了典型GPU渲染算法的并行度,所述GPU渲染算法在动画的每个帧中的像素上并行计算。为了解决这个问题,我们设计了我们的系统,并行处理一个像素的多个帧。这种新的并行化方法可以更好地利用GPU内存,还可以设计渲染真实的世界动画所需的高效核外算法。我们还开发了一个分析可见性算法来解决对象的深度冲突,减少所需的时间rescovery率和进一步暴露并行性。实验表明,我们能够处理非常大的场景,并提高运行时性能的数量级。
We present an efficient and scalable system that enables programmable motion effects on GPUs. Our system is based on the framework proposed by Schmid et al. [ SSBG10 ] that extends the concept of a surface shader to that of a programmable motion effect. While capable of expressing a variety of motion depiction styles, the execution of motion effect programs requires global knowledge about all portions of an object's surface that passes in front of a pixel during an arbitrarily long period of time, resulting in extremely high memory usage and significantly restricting the degree of parallelism of typical GPU rendering algorithms that parallelize computations over pixels in each frame of animations. To address this problem, we design our system to process multiple frames of a pixel in parallel. This new parallelization approach enables better utilization of GPU memory and also makes it possible to design an efficient out‐of‐core algorithm required in rendering real‐world animations. We also develop an analytical visibility algorithm to resolve depth conflicts of objects, reducing the required temporal resampling rate and further exposing parallelism. Experiments show that we are able to handle very large scenes and improve runtime performance up to an order of magnitude.
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