通过树小重构在GPU上构造K-d树

Vadim Bulavintsev, D. Zhdanov
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引用次数: 2

摘要

随着每一代新图形处理单元(gpu)的出现,将光线追踪算法卸载到gpu上变得更加可行。光线追踪的软硬件解决方案侧重于实现其基本组件,如构建和遍历边界体层次结构(BVH)。然而,全局照明算法,如光子映射方法,依赖于另一种加速结构,即k-d树。在这项工作中,我们采用了最先进的基于gpu的树小波重构bvh构建算法来实现k-d树。通过评估生成的k-d树的性能,我们表明适合于三角形bvh的树形优化启发式算法不适用于k-d点树。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constructing K-d Tree on GPU through Treelet Restructuring
With every new generation of graphics processing units (GPUs), offloading ray-tracing algorithms to GPUs becomes more feasible. Software-hardware solutions for ray-tracing focus on implementing its basic components, such as building and traversing bounding volume hierarchies (BVH). However, global illumination algorithms, such as photon mapping method, depend on another kind of acceleration structure, namely k-d trees. In this work, we adapt state-ofthe-art GPU-based BVH-building algorithm of treelet restructuring to k-d trees. By evaluating the performance of the resulting k-d tree, we show that treelet optimisation heuristic suitable for BVHs of triangles is inadequate for k-d trees of points.
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