Subspace Culling for Ray-Box Intersection

IF 1.4 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Proceedings of the ACM on computer graphics and interactive techniques Pub Date : 2023-05-12 DOI:10.1145/3585503
A. Yoshimura, T. Harada
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Abstract

Ray tracing is an essential operation for realistic image synthesis. The acceleration of ray tracing has been studied for a long period of time because algorithms such as light transport simulations require a large amount of ray tracing. One of the major approaches to accelerate the intersections is to use bounding volumes for early pruning for primitives in the volume. The axis-aligned bounding box is a popular bounding volume for ray tracing because of its simplicity and efficiency. However, the conservative bounding volume may produce extra empty space in addition to its content. Especially, primitives that are thin and diagonal to the axis give false-positive hits on the box volume due to the extra space. Although more complex bounding volumes such as oriented bounding boxes may reduce more false-positive hits, they are computationally expensive. In this paper, we propose a novel culling approach to reduce false-positive hits for the bounding box by embedding a binary voxel data structure to the volume. As a ray is represented as a conservative voxel volume as well in our approach, the ray--voxel intersection is cheaply done by bitwise AND operations. Our method is applicable to hierarchical data structures such as bounding volume hierarchy (BVH). It reduces false-positive hits due to the ray--box test and reduces the number of intersections during the traversal of BVH in ray tracing. We evaluate the reduction of intersections with several scenes and show the possibility of performance improvement despite the culling overhead. We also introduce a compression approach with a lookup table for our voxel data. We show that our compressed voxel data achieves significant false-positive reductions with a small amount of memory.
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光线盒相交的子空间消隐
光线跟踪是真实感图像合成的重要操作。光线跟踪的加速已经研究了很长一段时间,因为光传输模拟等算法需要大量的光线跟踪。加速相交的主要方法之一是使用边界体积对体积中的基本体进行早期修剪。轴对齐边界框是光线跟踪的常用边界体,因为它简单高效。然而,保守的边界体积除了其内容之外,还可能产生额外的空白空间。特别是,由于额外的空间,薄且与轴成对角线的基元会在长方体体积上产生假阳性命中。尽管更复杂的边界体积(如定向边界框)可能会减少更多的假阳性命中,但它们在计算上是昂贵的。在本文中,我们提出了一种新的剔除方法,通过在体积中嵌入二进制体素数据结构来减少边界框的假阳性命中。在我们的方法中,由于射线也被表示为保守的体素体积,因此射线与体素的相交可以通过逐位AND运算廉价地完成。我们的方法适用于层次数据结构,如边界体积层次(BVH)。它减少了由于光线盒测试而导致的假阳性命中,并减少了光线跟踪中BVH遍历过程中的交叉点数量。我们评估了几个场景中交叉口的减少,并显示了尽管存在剔除开销,但性能改进的可能性。我们还为我们的体素数据引入了一种具有查找表的压缩方法。我们表明,我们的压缩体素数据在少量内存的情况下实现了显著的假阳性减少。
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