The ray casting operation in the Monte Carlo ray tracing algorithm usually adopts a bounding volume hierarchy (BVH) to accelerate the process of finding intersections to evaluate visibility. However, its characteristics are irregular, with divergence in memory access and branch execution, so it cannot achieve maximum efficiency on GPUs. This paper proposes a novel Neural Intersection Function based on a multilayer perceptron whose core operation contains only dense matrix multiplication with predictable memory access. Our method is the first solution integrating the neural network-based approach and BVH-based ray tracing pipeline into one unified rendering framework. We can evaluate the visibility and occlusion of secondary rays without traversing the most irregular and time-consuming part of the BVH and thus accelerate ray casting. The experiments show the proposed method can reduce the secondary ray casting time for direct illumination by up to 35% compared to a BVH-based implementation and still preserve the image quality.

翻译：蒙特卡洛光线追踪算法中的射线投射操作通常采用层次包围盒（BVH）来加速求交过程以评估可见性。然而，该操作具有非规则特性，存在内存访问和分支执行的发散问题，因此在GPU上无法达到最大效率。本文提出一种基于多层感知器的新型神经交会函数，其核心运算仅包含具有可预测内存访问模式的稠密矩阵乘法。本方法是首个将基于神经网络的方法与基于BVH的光线追踪管线融合为统一渲染框架的解决方案。通过该方法，我们可在无需遍历BVH中最不规则且耗时部分的情况下评估次级射线的可见性与遮挡关系，从而加速射线投射。实验表明，与基于BVH的实现相比，所提方法可使直接光照中的次级射线投射时间最多减少35%，同时保持图像质量。