流式构建SAH kd树的经验

S. Popov, Johannes Günther, H. Seidel, P. Slusallek
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引用次数: 107

摘要

光线追踪性能最近取得进步的一个主要原因是使用了优化的加速结构,即基于表面积启发式(SAH)的kd树。虽然现有的算法可以在O(n log n)内构建这些搜索树,但对于更大的场景,构建时间仍然很高,并且不允许每帧重建kd树以支持动态变化。在本文中,我们提出了对以前的kd树构建算法的修改,这些算法在构建kd树期间显着增加了内存访问的一致性。此外,我们还提供了关于SAH成本函数的保守子抽样的理论和实践结果
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experiences with Streaming Construction of SAH KD-Trees
A major reason for the recent advancements in ray tracing performance is the use of optimized acceleration structures, namely kd-trees based on the surface area heuristic (SAH). Though algorithms exist to build these search trees in O(n log n), the construction times for larger scenes are still high and do not allow for rebuilding the kd-tree every frame to support dynamic changes. In this paper we propose modifications to previous kd-tree construction algorithms that significantly increase the coherence of memory accesses during construction of the kd-tree. Additionally we provide theoretical and practical results regarding conservatively sub-sampling of the SAH cost function
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