Cone Based Tree to Improve Ray Tracing

Kowshik Ahmed, Sofiul Azam Sony, Tanha Tanjimat, Mir Moynuddin Ahmed Shibly, Maimuna Rahman, T. Jabid
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Abstract

Real-time ray tracing has risen to be the topic of research in recent years. Different approaches have been introduced to optimize the algorithm. This study presented a new type of data structure, cone based i.e., CB-tree for traditional ray tracing. This data structure is applied for primary ray and shadow ray optimization. The cone’s definition has been modified by including information on the vertex, reference base point, and radius with the height of the cone. The advantages of this approach are that it avoids unnecessary ray object interaction calculation for both primary ray and shadow ray and finally, this is a procedure for faster image rendering calculation by maintaining the same level of details as traditional ray tracing. The study reveals that the proposed method CB-Tree shows the ratio of 0.021 and .007 for hard and soft shadow rays respectively, which is a much-improved performance than the traditional approach ratio 1. (performance would be good when less than 1).
基于锥的树改进光线追踪
实时光线追踪是近年来研究的热点。已经引入了不同的方法来优化算法。本文提出了一种新的数据结构,即基于锥的cb树,用于传统的射线追踪。该数据结构应用于主射线和阴影射线的优化。通过包含顶点、参考基点和圆锥体高度的半径等信息,修改了圆锥体的定义。这种方法的优点是它避免了不必要的射线和阴影射线的射线对象交互计算,最后,这是一个快速的图像渲染计算过程,通过保持与传统光线追踪相同的细节水平。研究表明,本文提出的CB-Tree方法对硬阴影光线和软阴影光线的提取比率分别为0.021和0.007,比传统方法的提取比率1有了很大的提高。(小于1时性能会很好)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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