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.

翻译：光线追踪是逼真图像合成的核心操作。由于光照传输模拟等算法需要执行大量光线追踪，其加速技术已历经长期研究。加速求交的主要方法之一是利用包围体对体积内基元进行早期裁剪。轴对齐包围盒因其简单高效成为光线追踪中常用的包围体，但这种保守的包围体可能在内容区域外产生额外空白空间。特别是当基元薄而倾斜于坐标轴时，多余空间会导致包围盒体积上出现误报命中。虽然有向包围盒等更复杂的包围体可减少误报命中，但其计算成本较高。本文提出一种新颖的裁剪方法，通过将二值体素数据结构嵌入包围盒体积来减少误报命中。由于光线同样被表示为保守的体素体积，光线-体素求交可通过按位与运算廉价实现。该方法适用于层次包围盒等层次化数据结构，能减少光线-包围盒测试导致的误报命中，并在光线追踪BVH遍历过程中降低求交次数。我们通过多组场景验证了求交次数的减少，表明尽管存在裁剪开销仍具有性能提升潜力。我们还引入了一种基于查找表的压缩方法处理体素数据，实验表明压缩后的体素数据能以极小内存开销实现显著的误报减少。