  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.
