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Octree-R: an adaptive octree for efficient ray tracing

机译:Octree-R:用于高效光线追踪的自适应八叉树

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Ray tracing requires many ray-object intersection tests. A way of reducing the number of ray-object intersection tests is to subdivide the space occupied by objects into many nonoverlapping subregions, called voxels, and to construct an octree for the subdivided space. We propose the Octree-R, an octree-variant data structure for efficient ray tracing. The algorithm for constructing the Octree-R first estimates the number of ray-object intersection tests. Then, it partitions the space along the plane that minimizes the estimated number of ray-object intersection tests. We present the results of experiments for verifying the effectiveness of the Octree-R. In the experiment, the Octree-R provides a 4% to 47% performance gain over the conventional octree. The result shows the more skewed the object distribution (as is typical for real data), the more performance gain the Octree-R achieves.
机译:射线追踪需要许多射线对象相交测试。减少射线对象相交测试次数的一种方法是将对象占据的空间细分为许多不重叠的子区域(称为体素),并为细分的空间构造八叉树。我们提出了Octree-R,这是一种八叉树变量数据结构,用于有效的光线跟踪。构造Octree-R的算法首先估算射线对象相交测试的次数。然后,它沿平面划分空间,从而使估计的射线对象相交测试次数最少。我们提出了用于验证Octree-R有效性的实验结果。在实验中,Octree-R的性能比传统八叉树提高了4%至47%。结果表明,对象分布越偏斜(对于真实数据而言通常如此),Octree-R可获得更高的性能增益。

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