关于为光线追踪构建快速kd树,以及在O(N log N)内完成

I. Wald, V. Havran
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引用次数: 394

摘要

尽管存在各种各样的光线追踪效率结构,但如今kd树似乎逐渐成为首选方法。特别是,使用成本估计函数(如表面积启发式(SAH))构建的kd树对于达到高性能似乎很重要。不幸的是,大多数构建这种树的算法的时间复杂度为O(N log2n),甚至O(N2)。在本文中,我们分析了构建用于光线追踪的良好kd-树的技术现状,并最终提出了一种在O(N log N)理论下界内构建SAH kd-树的算法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On building fast kd-Trees for Ray Tracing, and on doing that in O(N log N)
Though a large variety of efficiency structures for ray tracing exist, kd-trees today seem to slowly become the method of choice. In particular, kd-trees built with cost estimation functions such as a surface area heuristic (SAH) seem to be important for reaching high performance. Unfortunately, most algorithms for building such trees have a time complexity of O(N log2 N), or even O(N2). In this paper, we analyze the state of the art in building good kd-trees for ray tracing, and eventually propose an algorithm that builds SAH kd-trees in O(N log N), the theoretical lower bound
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